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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">vsp</journal-id><journal-title-group><journal-title xml:lang="ru">Вопросы современной педиатрии</journal-title><trans-title-group xml:lang="en"><trans-title>Current Pediatrics</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1682-5527</issn><issn pub-type="epub">1682-5535</issn><publisher><publisher-name>Издательство «ПедиатрЪ»</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.15690/vsp.v22i2.2557</article-id><article-id custom-type="elpub" pub-id-type="custom">vsp-3184</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>РЕДАКЦИОННАЯ СТАТЬЯ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>EDITORIAL</subject></subj-group></article-categories><title-group><article-title>Прозрачная отчетность о многофакторной предсказательной модели для индивидуального прогнозирования или диагностики (TRIPOD): разъяснения и уточнения</article-title><trans-title-group xml:lang="en"><trans-title>Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and Elaboration. Translation into Russian</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-2118-004X</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Moons</surname><given-names>K. G.M.</given-names></name><name name-style="western" xml:lang="en"><surname>Moons</surname><given-names>Karel G.M.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Julius Center for Health Sciences and Primary Care</p><p>PhD, Julius Centre for Health Sciences and Primary Care, UMC Utrecht</p><p>PO Box 85500, 3508 GA Utrecht, Утрехт</p></bio><bio xml:lang="en"><p>Julius Center for Health Sciences and Primary Care</p><p>PO Box 85500, 3508 GA Utrecht</p></bio><email xlink:type="simple">K.G.M.Moons@umcutrecht.nl</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-7183-4083</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Altman</surname><given-names>D. G.</given-names></name><name name-style="western" xml:lang="en"><surname>Altman</surname><given-names>Douglas G.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre</p><p>Oxford OX3 7LD, Оксфорд</p></bio><bio xml:lang="en"><p>Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre</p><p>Oxford OX3 7LD, Оксфорд</p></bio><xref ref-type="aff" rid="aff-2"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-4026-4345</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Reitsma</surname><given-names>J. B.</given-names></name><name name-style="western" xml:lang="en"><surname>Reitsma</surname><given-names>Johannes B.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Julius Center for Health Sciences and Primary Care</p><p>PO Box 85500, 3508 GA Utrecht, Утрехт</p></bio><bio xml:lang="en"><p>Julius Center for Health Sciences and Primary Care</p><p>PO Box 85500, 3508 GA Utrecht</p></bio><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-3118-6859</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Loannidis</surname><given-names>J. P.A.</given-names></name><name name-style="western" xml:lang="en"><surname>Loannidis</surname><given-names>John P.A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Stanford Prevention Research Center, School of Medicine</p><p>291 Campus Drive, Room LK3C02, Li Ka Shing Building, 3rd Floor, Stanford, CA 943055101, Стэнфорд</p></bio><bio xml:lang="en"><p>291 Campus Drive, Room LK3C02, Li Ka Shing Building, 3rd Floor, Stanford, CA 943055101</p></bio><xref ref-type="aff" rid="aff-3"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-5879-6193</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Macaskill</surname><given-names>P.</given-names></name><name name-style="western" xml:lang="en"><surname>Macaskill</surname><given-names>Petra</given-names></name></name-alternatives><bio xml:lang="ru"><p>Screening &amp; Test Evaluation Program (STEP), School of Public Health, Edward Ford Building (A27), Sydney Medical School</p><p>Sydney, NSW 2006, Сидней</p></bio><bio xml:lang="en"><p>Screening &amp; Test Evaluation Program (STEP), School of Public Health, Edward Ford Building (A27), Sydney Medical School</p><p>Sydney, NSW 2006</p></bio><xref ref-type="aff" rid="aff-4"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-7787-0122</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Steyerberg</surname><given-names>Е. W.</given-names></name><name name-style="western" xml:lang="en"><surname>Steyerberg</surname><given-names>Ewout W.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Department of Public Health</p><p>PO Box 2040, 3000 CA, Rotterdam, Роттердам</p></bio><bio xml:lang="en"><p>Department of Public Health</p><p>PO Box 2040, 3000 CA, Rotterdam</p></bio><xref ref-type="aff" rid="aff-5"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-1525-6503</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Vickers</surname><given-names>А. J.</given-names></name><name name-style="western" xml:lang="en"><surname>Vickers</surname><given-names>Andrew J.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Department of Epidemiology and Biostatistics</p><p>307 East 63rd Street, 2nd Floor, Box 44, New York, NY 10065, Нью-Йорк</p></bio><bio xml:lang="en"><p>Department of Epidemiology and Biostatistics</p><p>307 East 63rd Street, 2nd Floor, Box 44, New York, NY 10065</p></bio><xref ref-type="aff" rid="aff-6"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2200-039X</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Ransohoff</surname><given-names>D. F.</given-names></name><name name-style="western" xml:lang="en"><surname>Ransohoff</surname><given-names>David F.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Departments of Medicine and Epidemiology</p><p>4103 Bioinformatics, CB 7080, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7080, Чапел-Хилл</p></bio><bio xml:lang="en"><p>Departments of Medicine and Epidemiology</p><p>4103 Bioinformatics, CB 7080, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7080</p></bio><xref ref-type="aff" rid="aff-7"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2772-2316</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Collins</surname><given-names>G. S.</given-names></name><name name-style="western" xml:lang="en"><surname>Collins</surname><given-names>Gary S.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre</p><p>Oxford OX3 7LD, Оксфорд</p></bio><bio xml:lang="en"><p>Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre</p><p>Oxford OX3 7LD</p></bio><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>University Medical Center Utrecht</institution><country>Нидерланды</country></aff><aff xml:lang="en"><institution>University Medical Center Utrecht</institution><country>Netherlands</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>University of Oxford</institution><country>Великобритания</country></aff><aff xml:lang="en"><institution>University of Oxford</institution><country>United Kingdom</country></aff></aff-alternatives><aff-alternatives id="aff-3"><aff xml:lang="ru"><institution>Stanford University</institution><country>Соединённые Штаты Америки</country></aff><aff xml:lang="en"><institution>Stanford Prevention Research Center, School of Medicine, Stanford University</institution><country>United States</country></aff></aff-alternatives><aff-alternatives id="aff-4"><aff xml:lang="ru"><institution>University of Sydney</institution><country>Австралия</country></aff><aff xml:lang="en"><institution>University of Sydney</institution><country>Australia</country></aff></aff-alternatives><aff-alternatives id="aff-5"><aff xml:lang="ru"><institution>Erasmus MC-University Medical Center Rotterdam</institution><country>Нидерланды</country></aff><aff xml:lang="en"><institution>Erasmus MC-University Medical Center Rotterdam</institution><country>Netherlands</country></aff></aff-alternatives><aff-alternatives id="aff-6"><aff xml:lang="ru"><institution>Memorial Sloan Kettering Cancer Center</institution><country>Соединённые Штаты Америки</country></aff><aff xml:lang="en"><institution>Memorial Sloan Kettering Cancer Center</institution><country>United States</country></aff></aff-alternatives><aff-alternatives id="aff-7"><aff xml:lang="ru"><institution>University of North Carolina at Chapel Hill</institution><country>Соединённые Штаты Америки</country></aff><aff xml:lang="en"><institution>University of North Carolina at Chapel Hill</institution><country>United States</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2023</year></pub-date><pub-date pub-type="epub"><day>07</day><month>05</month><year>2023</year></pub-date><volume>22</volume><issue>2</issue><fpage>109</fpage><lpage>187</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Moons K.G., Altman D.G., Reitsma J.B., Loannidis J.P., Macaskill P., Steyerberg Е.W., Vickers А.J., Ransohoff D.F., Collins G.S., 2023</copyright-statement><copyright-year>2023</copyright-year><copyright-holder xml:lang="ru">Moons K.G., Altman D.G., Reitsma J.B., Loannidis J.P., Macaskill P., Steyerberg Е.W., Vickers А.J., Ransohoff D.F., Collins G.S.</copyright-holder><copyright-holder xml:lang="en">Moons K.G., Altman D.G., Reitsma J.B., Loannidis J.P., Macaskill P., Steyerberg E.W., Vickers A.J., Ransohoff D.F., Collins G.S.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://vsp.spr-journal.ru/jour/article/view/3184">https://vsp.spr-journal.ru/jour/article/view/3184</self-uri><abstract><p>.Руководство TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) содержит контрольный перечень из 22 пунктов рекомендаций, предложенных для повышения качества отчетности по исследованиям, в которых разрабатывали, проверяли или обновляли предсказательные модели для диагностики или прогнозирования. Руководство TRIPOD направлено на повышение прозрачности отчета об исследовании предсказательной модели независимо от использованных методов. Этот документ с пояснениями и уточнениями включает обоснование, разъяснения значений каждого пункта рекомендаций, обсуждение важности прозрачной отчетности для оценки риска систематических ошибок и клинической полезности предсказательной модели. Каждая рекомендация руководства TRIPOD подробно объясняется, приводятся опубликованные примеры правильного представления результатов. Документ также содержит ценную справочную информацию, которую следует учитывать при разработке, проведении и анализе исследований предсказательных моделей. Рекомендуем авторам включать в свои работы все пункты контрольного перечня, что облегчит оценку исследования редакторами, рецензентами, читателями и исследователями, проводящими систематическое обобщение результатов таких исследований. Контрольный перечень TRIPOD также доступен по адресу: <ext-link xlink:href="http://www.tripod-statement.org." ext-link-type="uri">www.tripod-statement.org. </ext-link></p><p>Данная статья является переводом на русский язык. Оригинальная статья опубликована в Annals of Internal Medicine. 2015;162(1):W1–W73. doi: https://doi.org/10.7326/M14-0698. Перевод и повторная публикация осуществлены с разрешения правообладателя. Перевод и научное редактирование выполнены д.м.н. Р.Т. Сайгито вым (ORCID: https://orcid.org/0000-0002-8915-6153). Перевод впервые опубликован в Digital Diagnostics. doi: https://doi.org/10.17816/DD110794. Публикуется с незначительными изменениями, связанными с литературным редактированием текста перевода.</p></abstract><trans-abstract xml:lang="en"><p>The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org. This article is the translation in to Russian by Dr. Ruslan Saygitov (ORCID: https://orcid.org/0000-0002-8915-6153) from the original published in [Ann Intern Med. 2015;162:W1-W73. doi: <ext-link xlink:href="https://doi.org/10.7326/M14-0698" ext-link-type="uri">https://doi.org/10.7326/M14-0698</ext-link>].</p></trans-abstract><funding-group><funding-statement xml:lang="ru">Разработка контрольного перечня и рекомендаций выполнена без прямой спонсорской поддержки. Заседание в июне 2011 г. проведено при частичной поддержке National Institute for Health Research Senior Investigator Award во главе с доктором Altman, а также грантов со стороны Cancer Research UK (C5529) и Netherlands Organization for Scientific Research (ZONMW   918.10.615   и   91208004).   Доктора   Collins и Altman получили частичную финансовую поддержку со стороны Medical Research Council (G1100513). Доктор Altman является членом Medical Research Council Prognosis Research Strategy (PROGRESS) Partnership (G0902393/99558)</funding-statement><funding-statement xml:lang="en">There was no explicit funding for the development of this checklist and guidance document. The consensus meeting in June 2011 was partially funded by a National Institute for Health Research Senior Investigator Award held by Dr. Altman, Cancer Research UK (grant C5529), and the Netherlands Organization for Scientific Research (ZONMW 918.10.615 and 91208004). Drs. Collins and Altman are funded in part by the Medical Research Council (grant G1100513). Dr. Altman is a member of the Medical Research Council Prognosis Research Strategy PROGRESS) Partnership (G0902393/99558)</funding-statement></funding-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Moons KG, Royston P, Vergouwe Y, et al. Prognosis and prognostic research: what, why, and how? BMJ. 2009;338:b375. doi: https://doi.org/10.1136/bmj.b375</mixed-citation><mixed-citation xml:lang="en">Moons KG, Royston P, Vergouwe Y, et al. Prognosis and prognostic research: what, why, and how? BMJ. 2009;338:b375. doi: https://doi.org/10.1136/bmj.b375</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. New York: Springer; 2009.</mixed-citation><mixed-citation xml:lang="en">Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. New York: Springer; 2009.</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Wasson JH, Sox HC, Neff RK, Goldman L. Clinical prediction rules. Applications and methodological standards. N Engl J Med. 1985;313(13):793–799. doi: https://doi.org/10.1056/NEJM198509263131306</mixed-citation><mixed-citation xml:lang="en">Wasson JH, Sox HC, Neff RK, Goldman L. Clinical prediction rules. Applications and methodological standards. N Engl J Med. 1985;313(13):793–799. doi: https://doi.org/10.1056/NEJM198509263131306</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Dorresteijn JA, Visseren FL, Ridker PM, et al. Estimating treatment effects for individual patients based on the results of randomised clinical trials. BMJ. 2011;343:d5888. doi: https://doi.org/10.1136/bmj.d5888</mixed-citation><mixed-citation xml:lang="en">Dorresteijn JA, Visseren FL, Ridker PM, et al. Estimating treatment effects for individual patients based on the results of randomised clinical trials. BMJ. 2011;343:d5888. doi: https://doi.org/10.1136/bmj.d5888</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Hayward RA, Kent DM, Vijan S, Hofer TP. Multivariable risk prediction can greatly enhance the statistical power of clinical trial subgroup analysis. BMC Med Res Methodol. 2006;6:18. doi: https://doi.org/10.1186/1471-2288-6-18</mixed-citation><mixed-citation xml:lang="en">Hayward RA, Kent DM, Vijan S, Hofer TP. Multivariable risk prediction can greatly enhance the statistical power of clinical trial subgroup analysis. BMC Med Res Methodol. 2006;6:18. doi: https://doi.org/10.1186/1471-2288-6-18</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Kattan MW, Vickers AJ. Incorporating predictions of individual patient risk in clinical trials. Urol Oncol. 2004;22(4):348–352. doi: https://doi.org/10.1016/j.urolonc.2004.04.012</mixed-citation><mixed-citation xml:lang="en">Kattan MW, Vickers AJ. Incorporating predictions of individual patient risk in clinical trials. Urol Oncol. 2004;22(4):348–352. doi: https://doi.org/10.1016/j.urolonc.2004.04.012</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Kent DM, Hayward RA. Limitations of applying summary results of clinical trials to individual patients: the need for risk stratification. JAMA. 2007;298(10):1209–1212. doi: https://doi.org/10.1001/jama.298.10.1209</mixed-citation><mixed-citation xml:lang="en">Kent DM, Hayward RA. Limitations of applying summary results of clinical trials to individual patients: the need for risk stratification. JAMA. 2007;298(10):1209–1212. doi: https://doi.org/10.1001/jama.298.10.1209</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Riley RD, Hayden JA, Steyerberg EW, et al. Prognosis Research Strategy (PROGRESS) 2: prognostic factor research. PLoS Med. 2013;10(2):e1001380. doi: https://doi.org/10.1371/journal.pmed.1001380</mixed-citation><mixed-citation xml:lang="en">Riley RD, Hayden JA, Steyerberg EW, et al. Prognosis Research Strategy (PROGRESS) 2: prognostic factor research. PLoS Med. 2013;10(2):e1001380. doi: https://doi.org/10.1371/journal.pmed.1001380</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Steyerberg EW, Moons KG, van der Windt DA, et al. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med. 2013;10(2):e1001381. doi: https://doi.org/10.1371/journal.pmed.1001381</mixed-citation><mixed-citation xml:lang="en">Steyerberg EW, Moons KG, van der Windt DA, et al. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med. 2013;10(2):e1001381. doi: https://doi.org/10.1371/journal.pmed.1001381</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Royston P, Moons KG, Altman DG, Vergouwe Y. Prognosis and prognostic research: developing a prognostic model. BMJ. 2009; 338:b604. doi: https://doi.org/10.1136/bmj.b604</mixed-citation><mixed-citation xml:lang="en">Royston P, Moons KG, Altman DG, Vergouwe Y. Prognosis and prognostic research: developing a prognostic model. BMJ. 2009; 338:b604. doi: https://doi.org/10.1136/bmj.b604</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Collins GS, Altman DG. Identifying patients with undetected renal tract cancer in primary care: an independent and external validation of QCancer® (Renal) prediction model. Cancer Epidemiol. 2013;37(2):115–120. doi: https://doi.org/10.1016/j.canep.2012.11.005</mixed-citation><mixed-citation xml:lang="en">Collins GS, Altman DG. Identifying patients with undetected renal tract cancer in primary care: an independent and external validation of QCancer® (Renal) prediction model. Cancer Epidemiol. 2013;37(2):115–120. doi: https://doi.org/10.1016/j.canep.2012.11.005</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Harrell FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996; 15(4):361–387. doi: https://doi.org/10.1002/(SICI)1097-0258(19960229)15:43.0.CO;2-4</mixed-citation><mixed-citation xml:lang="en">Harrell FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996; 15(4):361–387. doi: https://doi.org/10.1002/(SICI)1097-0258(19960229)15:43.0.CO;2-4</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Canet J, Gallart L, Gomar C, et al. Prediction of postoperative pulmonary complications in a population-based surgical cohort. Anesthesiology. 2010;113(6):1338–1350. doi: https://doi.org/10.1097/ALN.0b013e3181fc6e0a</mixed-citation><mixed-citation xml:lang="en">Canet J, Gallart L, Gomar C, et al. Prediction of postoperative pulmonary complications in a population-based surgical cohort. Anesthesiology. 2010;113(6):1338–1350. doi: https://doi.org/10.1097/ALN.0b013e3181fc6e0a</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Nashef SA, Roques F, Sharples LD, et al. EuroSCORE II. Eur J Cardiothorac Surg. 2012;41:734–744; discussion 744–745. doi: https://doi.org/10.1093/ejcts/ezs043</mixed-citation><mixed-citation xml:lang="en">Nashef SA, Roques F, Sharples LD, et al. EuroSCORE II. Eur J Cardiothorac Surg. 2012;41:734–744; discussion 744–745. doi: https://doi.org/10.1093/ejcts/ezs043</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Schulze MB, Hoffmann K, Boeing H, et al. An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes Care. 2007;30(3): 510–515. doi: https://doi.org/10.2337/dc06-2089</mixed-citation><mixed-citation xml:lang="en">Schulze MB, Hoffmann K, Boeing H, et al. An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes Care. 2007;30(3): 510–515. doi: https://doi.org/10.2337/dc06-2089</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Hippisley-Cox J, Coupland C, Robson J, et al. Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ. 2009;338:b880. doi: https://doi.org/10.1136/bmj.b880</mixed-citation><mixed-citation xml:lang="en">Hippisley-Cox J, Coupland C, Robson J, et al. Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ. 2009;338:b880. doi: https://doi.org/10.1136/bmj.b880</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">D’Agostino RB, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743–753. doi: https://doi.org/10.1161/CIRCULATIONAHA.107.699579</mixed-citation><mixed-citation xml:lang="en">D’Agostino RB, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743–753. doi: https://doi.org/10.1161/CIRCULATIONAHA.107.699579</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">North RA, McCowan LM, Dekker GA, et al. Clinical risk prediction for pre-eclampsia in nulliparous women: development of model in international prospective cohort. BMJ. 2011;342:d1875. doi: https://doi.org/10.1136/bmj.d1875</mixed-citation><mixed-citation xml:lang="en">North RA, McCowan LM, Dekker GA, et al. Clinical risk prediction for pre-eclampsia in nulliparous women: development of model in international prospective cohort. BMJ. 2011;342:d1875. doi: https://doi.org/10.1136/bmj.d1875</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Altman DG, Vergouwe Y, Royston P, Moons KG. Prognosis and prognostic research: validating a prognostic model. BMJ. 2009; 338:b605. doi: https://doi.org/10.1136/bmj.b605</mixed-citation><mixed-citation xml:lang="en">Altman DG, Vergouwe Y, Royston P, Moons KG. Prognosis and prognostic research: validating a prognostic model. BMJ. 2009; 338:b605. doi: https://doi.org/10.1136/bmj.b605</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Moons KG, Kengne AP, Grobbee DE, et al. Risk prediction models: II. External validation, model updating, and impact assessment. Heart. 2012;98(9):691–698. doi: https://doi.org/10.1136/heartjnl-2011-301247</mixed-citation><mixed-citation xml:lang="en">Moons KG, Kengne AP, Grobbee DE, et al. Risk prediction models: II. External validation, model updating, and impact assessment. Heart. 2012;98(9):691–698. doi: https://doi.org/10.1136/heartjnl-2011-301247</mixed-citation></citation-alternatives></ref><ref id="cit21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">Toll DB, Janssen KJ, Vergouwe Y, Moons KG. Validation, updating and impact of clinical prediction rules: a review. J Clin Epidemiol. 2008;61(11): 1085–1094. doi: https://doi.org/10.1016/j.jclinepi.2008.04.008</mixed-citation><mixed-citation xml:lang="en">Toll DB, Janssen KJ, Vergouwe Y, Moons KG. Validation, updating and impact of clinical prediction rules: a review. J Clin Epidemiol. 2008;61(11): 1085–1094. doi: https://doi.org/10.1016/j.jclinepi.2008.04.008</mixed-citation></citation-alternatives></ref><ref id="cit22"><label>22</label><citation-alternatives><mixed-citation xml:lang="ru">Steyerberg EW, Pencina MJ, Lingsma HF, et al. Assessing the incremental value of diagnostic and prognostic markers: a review and illustration. Eur J Clin Invest. 2012;42(2):216–228. doi: https://doi.org/10.1111/j.1365-2362.2011.02562.x</mixed-citation><mixed-citation xml:lang="en">Steyerberg EW, Pencina MJ, Lingsma HF, et al. Assessing the incremental value of diagnostic and prognostic markers: a review and illustration. Eur J Clin Invest. 2012;42(2):216–228. doi: https://doi.org/10.1111/j.1365-2362.2011.02562.x</mixed-citation></citation-alternatives></ref><ref id="cit23"><label>23</label><citation-alternatives><mixed-citation xml:lang="ru">Steyerberg EW, Bleeker SE, Moll HA, et al. Internal and external validation of predictive models: a simulation study of bias and precision in small samples. J Clin Epidemiol. 2003;56(5):441–447. doi: https://doi.org/10.1016/s0895-4356(03)00047-7</mixed-citation><mixed-citation xml:lang="en">Steyerberg EW, Bleeker SE, Moll HA, et al. Internal and external validation of predictive models: a simulation study of bias and precision in small samples. J Clin Epidemiol. 2003;56(5):441–447. doi: https://doi.org/10.1016/s0895-4356(03)00047-7</mixed-citation></citation-alternatives></ref><ref id="cit24"><label>24</label><citation-alternatives><mixed-citation xml:lang="ru">Steyerberg EW, Eijkemans MJ, Harrell FE, Habbema JD. Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets. Stat Med. 2000;19(8):1059–1079. doi: https://doi.org/10.1002/(sici)1097-0258(20000430)19:83.0.co;2-0</mixed-citation><mixed-citation xml:lang="en">Steyerberg EW, Eijkemans MJ, Harrell FE, Habbema JD. Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets. Stat Med. 2000;19(8):1059–1079. doi: https://doi.org/10.1002/(sici)1097-0258(20000430)19:83.0.co;2-0</mixed-citation></citation-alternatives></ref><ref id="cit25"><label>25</label><citation-alternatives><mixed-citation xml:lang="ru">Steyerberg EW, Eijkemans MJ, Harrell FE, Habbema JD. Prognostic modeling with logistic regression analysis: in search of a sensible strategy in small data sets. Med Decis Making. 2001;21(1):45–56. doi: https://doi.org/10.1177/0272989X0102100106</mixed-citation><mixed-citation xml:lang="en">Steyerberg EW, Eijkemans MJ, Harrell FE, Habbema JD. Prognostic modeling with logistic regression analysis: in search of a sensible strategy in small data sets. Med Decis Making. 2001;21(1):45–56. doi: https://doi.org/10.1177/0272989X0102100106</mixed-citation></citation-alternatives></ref><ref id="cit26"><label>26</label><citation-alternatives><mixed-citation xml:lang="ru">Altman DG, Royston P. What do we mean by validating a prognostic model? Stat Med. 2000;19:453–473. doi: https://doi.org/10.1002/(sici)1097-0258(20000229)19:43.0.co;2-5</mixed-citation><mixed-citation xml:lang="en">Altman DG, Royston P. What do we mean by validating a prognostic model? Stat Med. 2000;19:453–473. doi: https://doi.org/10.1002/(sici)1097-0258(20000229)19:43.0.co;2-5</mixed-citation></citation-alternatives></ref><ref id="cit27"><label>27</label><citation-alternatives><mixed-citation xml:lang="ru">Ioannidis JPA, Khoury MJ. Improving validation practices in “omics” research. Science. 2011;334(6060):1230–1232. doi: https://doi.org/10.1126/science.1211811</mixed-citation><mixed-citation xml:lang="en">Ioannidis JPA, Khoury MJ. Improving validation practices in “omics” research. Science. 2011;334(6060):1230–1232. doi: https://doi.org/10.1126/science.1211811</mixed-citation></citation-alternatives></ref><ref id="cit28"><label>28</label><citation-alternatives><mixed-citation xml:lang="ru">Justice AC, Covinsky KE, Berlin JA. Assessing the generalizability of prognostic information. Ann Intern Med. 1999;130(6):515–524. doi: https://doi.org/10.7326/0003-4819-130-6-199903160-00016</mixed-citation><mixed-citation xml:lang="en">Justice AC, Covinsky KE, Berlin JA. Assessing the generalizability of prognostic information. Ann Intern Med. 1999;130(6):515–524. doi: https://doi.org/10.7326/0003-4819-130-6-199903160-00016</mixed-citation></citation-alternatives></ref><ref id="cit29"><label>29</label><citation-alternatives><mixed-citation xml:lang="ru">McGinn TG, Guyatt GH, Wyer PC, et al. Users’ guides to the medical literature: XXII: how to use articles about clinical decision rules. Evidence-Based Medicine Working Group. JAMA. 2000;284: 79–84. doi: https://doi.org/10.1001/jama.284.1.79</mixed-citation><mixed-citation xml:lang="en">McGinn TG, Guyatt GH, Wyer PC, et al. Users’ guides to the medical literature: XXII: how to use articles about clinical decision rules. Evidence-Based Medicine Working Group. JAMA. 2000;284: 79–84. doi: https://doi.org/10.1001/jama.284.1.79</mixed-citation></citation-alternatives></ref><ref id="cit30"><label>30</label><citation-alternatives><mixed-citation xml:lang="ru">Taylor JM, Ankesrt DP, Andridge RR. Validation of biomarkerbased risk prediction models. Clin Cancer Res. 2008;14(19): 5977–5983. doi: https://doi.org/10.1158/1078-0432.CCR-07-4534</mixed-citation><mixed-citation xml:lang="en">Taylor JM, Ankesrt DP, Andridge RR. Validation of biomarkerbased risk prediction models. Clin Cancer Res. 2008;14(19): 5977–5983. doi: https://doi.org/10.1158/1078-0432.CCR-07-4534</mixed-citation></citation-alternatives></ref><ref id="cit31"><label>31</label><citation-alternatives><mixed-citation xml:lang="ru">Janssen KJ, Moons KG, Kalkman CJ, et al. Updating methods improved the performance of a clinical prediction model in new patients. J Clin Epidemiol. 2008;61(1):76–86. doi: https://doi.org/10.1016/j.jclinepi.2007.04.018</mixed-citation><mixed-citation xml:lang="en">Janssen KJ, Moons KG, Kalkman CJ, et al. Updating methods improved the performance of a clinical prediction model in new patients. J Clin Epidemiol. 2008;61(1):76–86. doi: https://doi.org/10.1016/j.jclinepi.2007.04.018</mixed-citation></citation-alternatives></ref><ref id="cit32"><label>32</label><citation-alternatives><mixed-citation xml:lang="ru">Steyerberg EW, Harrell FE, Borsboom GJ, et al. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001;54(8):774–871. doi: https://doi.org/10.1016/s0895-4356(01)00341-9</mixed-citation><mixed-citation xml:lang="en">Steyerberg EW, Harrell FE, Borsboom GJ, et al. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001;54(8):774–871. doi: https://doi.org/10.1016/s0895-4356(01)00341-9</mixed-citation></citation-alternatives></ref><ref id="cit33"><label>33</label><citation-alternatives><mixed-citation xml:lang="ru">Reilly BM, Evans AT. Translating clinical research into clinical practice: impact of using prediction rules to make decisions. Ann Intern Med. 2006;144(3):201–209. doi: https://doi.org/10.7326/0003-4819-144-3-200602070-00009</mixed-citation><mixed-citation xml:lang="en">Reilly BM, Evans AT. Translating clinical research into clinical practice: impact of using prediction rules to make decisions. Ann Intern Med. 2006;144(3):201–209. doi: https://doi.org/10.7326/0003-4819-144-3-200602070-00009</mixed-citation></citation-alternatives></ref><ref id="cit34"><label>34</label><citation-alternatives><mixed-citation xml:lang="ru">Bouwmeester W, Zuithoff NP, Mallett S, et al. Reporting and methods in clinical prediction research: a systematic review. PLoS Med. 2012;9(5):1–12. doi: https://doi.org/10.1371/journal.pmed.1001221</mixed-citation><mixed-citation xml:lang="en">Bouwmeester W, Zuithoff NP, Mallett S, et al. Reporting and methods in clinical prediction research: a systematic review. PLoS Med. 2012;9(5):1–12. doi: https://doi.org/10.1371/journal.pmed.1001221</mixed-citation></citation-alternatives></ref><ref id="cit35"><label>35</label><citation-alternatives><mixed-citation xml:lang="ru">Rabar S, Lau R, O’Flynn N, et al. Risk assessment of fragility fractures: summary of NICE guidance. BMJ. 2012;345:e3698. doi: https://doi.org/10.1136/bmj.e3698</mixed-citation><mixed-citation xml:lang="en">Rabar S, Lau R, O’Flynn N, et al. Risk assessment of fragility fractures: summary of NICE guidance. BMJ. 2012;345:e3698. doi: https://doi.org/10.1136/bmj.e3698</mixed-citation></citation-alternatives></ref><ref id="cit36"><label>36</label><citation-alternatives><mixed-citation xml:lang="ru">National Institute for Health and Care Excellence. Lipid modification: cardiovascular risk assessment and the modification of blood lipids for the primary and secondary prevention of cardiovascular disease. Clinical guideline CG67. London: National Institute for Health and Care Excellence; 2008. Available online: http://guidance.nice.org.uk/CG67. Accessed on October 30, 2011.</mixed-citation><mixed-citation xml:lang="en">National Institute for Health and Care Excellence. Lipid modification: cardiovascular risk assessment and the modification of blood lipids for the primary and secondary prevention of cardiovascular disease. Clinical guideline CG67. London: National Institute for Health and Care Excellence; 2008. Available online: http://guidance.nice.org.uk/CG67. Accessed on October 30, 2011.</mixed-citation></citation-alternatives></ref><ref id="cit37"><label>37</label><citation-alternatives><mixed-citation xml:lang="ru">National Osteoporosis Foundation. Clinician’s guide to prevention and treatment of osteoporosis. Washington DC: National Osteoporsis Foundation; 2010. Available online: http://nof.org/hcp/clinicians-guide. Accessed on January 17, 2013.</mixed-citation><mixed-citation xml:lang="en">National Osteoporosis Foundation. Clinician’s guide to prevention and treatment of osteoporosis. Washington DC: National Osteoporsis Foundation; 2010. Available online: http://nof.org/hcp/clinicians-guide. Accessed on January 17, 2013.</mixed-citation></citation-alternatives></ref><ref id="cit38"><label>38</label><citation-alternatives><mixed-citation xml:lang="ru">National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third report of the National Cholesterol Education Program (NCEP) Expert Panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) final report. Circulation. 2002;106:3143–3421.</mixed-citation><mixed-citation xml:lang="en">National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third report of the National Cholesterol Education Program (NCEP) Expert Panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) final report. Circulation. 2002;106:3143–3421.</mixed-citation></citation-alternatives></ref><ref id="cit39"><label>39</label><citation-alternatives><mixed-citation xml:lang="ru">Goldstein LB, Adams R, Alberts MJ, et al. Primary prevention of ischemic stroke: a guideline from the American Heart Association/ American Stroke Association Stroke Council: cosponsored by the Atherosclerotic Peripheral Vascular Disease Interdisciplinary Working Group; Cardiovascular Nursing Council; Clinical Cardiology Council; Nutrition, Physical Activity, and Metabolism Council; and the Quality of Care and Outcomes Research Interdisciplinary Working Group. Circulation. 2006;113:e873–e923. doi: https://doi.org/10.1161/01.STR.0000223048.70103.F1</mixed-citation><mixed-citation xml:lang="en">Goldstein LB, Adams R, Alberts MJ, et al. Primary prevention of ischemic stroke: a guideline from the American Heart Association/ American Stroke Association Stroke Council: cosponsored by the Atherosclerotic Peripheral Vascular Disease Interdisciplinary Working Group; Cardiovascular Nursing Council; Clinical Cardiology Council; Nutrition, Physical Activity, and Metabolism Council; and the Quality of Care and Outcomes Research Interdisciplinary Working Group. Circulation. 2006;113:e873–e923. doi: https://doi.org/10.1161/01.STR.0000223048.70103.F1</mixed-citation></citation-alternatives></ref><ref id="cit40"><label>40</label><citation-alternatives><mixed-citation xml:lang="ru">Lackland DT, Elkind MS, D’Agostino R, et al. Inclusion of stroke in cardiovascular risk prediction instruments: a statement for healthcare professionals from the American Heart Association/ American Stroke Association. Stroke. 2012;43(7):1998–2027. doi: https://doi.org/10.1161/STR.0b013e31825bcdac</mixed-citation><mixed-citation xml:lang="en">Lackland DT, Elkind MS, D’Agostino R, et al. Inclusion of stroke in cardiovascular risk prediction instruments: a statement for healthcare professionals from the American Heart Association/ American Stroke Association. Stroke. 2012;43(7):1998–2027. doi: https://doi.org/10.1161/STR.0b013e31825bcdac</mixed-citation></citation-alternatives></ref><ref id="cit41"><label>41</label><citation-alternatives><mixed-citation xml:lang="ru">Perel P, Edwards P, Wentz R, Roberts I. Systematic review of prognostic models in traumatic brain injury. BMC Med Inform Decis Mak. 2006;6:38. doi: https://doi.org/10.1186/1472-6947-6-38</mixed-citation><mixed-citation xml:lang="en">Perel P, Edwards P, Wentz R, Roberts I. Systematic review of prognostic models in traumatic brain injury. BMC Med Inform Decis Mak. 2006;6:38. doi: https://doi.org/10.1186/1472-6947-6-38</mixed-citation></citation-alternatives></ref><ref id="cit42"><label>42</label><citation-alternatives><mixed-citation xml:lang="ru">Shariat SF, Karakiewicz PI, Margulis V, Kattan MW. Inventory of prostate cancer predictive tools. Curr Opin Urol. 2008;18(3): 279–296. doi: https://doi.org/10.1097/MOU.0b013e3282f9b3e5</mixed-citation><mixed-citation xml:lang="en">Shariat SF, Karakiewicz PI, Margulis V, Kattan MW. Inventory of prostate cancer predictive tools. Curr Opin Urol. 2008;18(3): 279–296. doi: https://doi.org/10.1097/MOU.0b013e3282f9b3e5</mixed-citation></citation-alternatives></ref><ref id="cit43"><label>43</label><citation-alternatives><mixed-citation xml:lang="ru">Altman DG. Prognostic models: a methodological framework and review of models for breast cancer. Cancer Invest. 2009;27(3): 235–243. doi: https://doi.org/10.1080/07357900802572110</mixed-citation><mixed-citation xml:lang="en">Altman DG. Prognostic models: a methodological framework and review of models for breast cancer. Cancer Invest. 2009;27(3): 235–243. doi: https://doi.org/10.1080/07357900802572110</mixed-citation></citation-alternatives></ref><ref id="cit44"><label>44</label><citation-alternatives><mixed-citation xml:lang="ru">van Dieren S, Beulens JW, Kengne AP, et al. Prediction models for the risk of cardiovascular disease in patients with type 2 diabetes: a systematic review. Heart. 2012;98(5):360–369. doi: https://doi.org/10.1136/heartjnl-2011-300734</mixed-citation><mixed-citation xml:lang="en">van Dieren S, Beulens JW, Kengne AP, et al. Prediction models for the risk of cardiovascular disease in patients with type 2 diabetes: a systematic review. Heart. 2012;98(5):360–369. doi: https://doi.org/10.1136/heartjnl-2011-300734</mixed-citation></citation-alternatives></ref><ref id="cit45"><label>45</label><citation-alternatives><mixed-citation xml:lang="ru">Collins GS, Mallett S, Omar O, Yu LM. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med. 2011;9:103. doi: https://doi.org/10.1186/1741-7015-9-103</mixed-citation><mixed-citation xml:lang="en">Collins GS, Mallett S, Omar O, Yu LM. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med. 2011;9:103. doi: https://doi.org/10.1186/1741-7015-9-103</mixed-citation></citation-alternatives></ref><ref id="cit46"><label>46</label><citation-alternatives><mixed-citation xml:lang="ru">Ettema RG, Peelen LM, Schuurmans MJ, et al. Prediction mode ls for prolonged intensive care unit stay after cardiac surgery: systematic review and validation study. Circulation. 2010;122:682–689. doi: https://doi.org/10.1161/CIRCULATIONAHA.109.926808</mixed-citation><mixed-citation xml:lang="en">Ettema RG, Peelen LM, Schuurmans MJ, et al. Prediction mode ls for prolonged intensive care unit stay after cardiac surgery: systematic review and validation study. Circulation. 2010;122:682–689. doi: https://doi.org/10.1161/CIRCULATIONAHA.109.926808</mixed-citation></citation-alternatives></ref><ref id="cit47"><label>47</label><citation-alternatives><mixed-citation xml:lang="ru">Collins GS, Moons KG. Comparing risk prediction models. BMJ. 2012;344:e3186.</mixed-citation><mixed-citation xml:lang="en">Collins GS, Moons KG. Comparing risk prediction models. BMJ. 2012;344:e3186.</mixed-citation></citation-alternatives></ref><ref id="cit48"><label>48</label><citation-alternatives><mixed-citation xml:lang="ru">Siontis GC, Tzoulaki I, Siontis KC, Ioannidis JP. Comparisons of established risk prediction models for cardiovascular disease: systematic review. BMJ. 2012;344:e3318. doi: https://doi.org/10.1136/bmj.e3186</mixed-citation><mixed-citation xml:lang="en">Siontis GC, Tzoulaki I, Siontis KC, Ioannidis JP. Comparisons of established risk prediction models for cardiovascular disease: systematic review. BMJ. 2012;344:e3318. doi: https://doi.org/10.1136/bmj.e3186</mixed-citation></citation-alternatives></ref><ref id="cit49"><label>49</label><citation-alternatives><mixed-citation xml:lang="ru">Seel RT, Steyerberg EW, Malec JF, et al. Developing and evaluating prediction models in rehabilitation populations. Arch Phys Med Rehabil. 2012;93 8 Suppl S138–S153. doi: https://doi.org/10.1016/j.apmr.2012.04.021</mixed-citation><mixed-citation xml:lang="en">Seel RT, Steyerberg EW, Malec JF, et al. Developing and evaluating prediction models in rehabilitation populations. Arch Phys Med Rehabil. 2012;93 8 Suppl S138–S153. doi: https://doi.org/10.1016/j.apmr.2012.04.021</mixed-citation></citation-alternatives></ref><ref id="cit50"><label>50</label><citation-alternatives><mixed-citation xml:lang="ru">Green SM, Schriger DL, Yealy DM. Methodologic standards for interpreting clinical decision rules in emergency medicine: 2014 update. Ann Emerg Med. 2014;64:286–291. doi: https://doi.org/10.1016/j.annemergmed.2014.01.016</mixed-citation><mixed-citation xml:lang="en">Green SM, Schriger DL, Yealy DM. Methodologic standards for interpreting clinical decision rules in emergency medicine: 2014 update. Ann Emerg Med. 2014;64:286–291. doi: https://doi.org/10.1016/j.annemergmed.2014.01.016</mixed-citation></citation-alternatives></ref><ref id="cit51"><label>51</label><citation-alternatives><mixed-citation xml:lang="ru">Laine C, Goodman SN, Griswold ME, Sox HC. Reproducible research: moving toward research the public can really trust. Ann Intern Med. 2007;146:450–453. doi: https://doi.org/10.7326/0003-4819-146-6-200703200-00154</mixed-citation><mixed-citation xml:lang="en">Laine C, Goodman SN, Griswold ME, Sox HC. Reproducible research: moving toward research the public can really trust. Ann Intern Med. 2007;146:450–453. doi: https://doi.org/10.7326/0003-4819-146-6-200703200-00154</mixed-citation></citation-alternatives></ref><ref id="cit52"><label>52</label><citation-alternatives><mixed-citation xml:lang="ru">Groves T, Godlee F. Open science and reproducible research. BMJ. 2012;344:e4383. doi: https://doi.org/10.1136/bmj.e4383</mixed-citation><mixed-citation xml:lang="en">Groves T, Godlee F. Open science and reproducible research. BMJ. 2012;344:e4383. doi: https://doi.org/10.1136/bmj.e4383</mixed-citation></citation-alternatives></ref><ref id="cit53"><label>53</label><citation-alternatives><mixed-citation xml:lang="ru">Collins GS, Omar O, Shanyinde M, Yu LM. A systematic review finds prediction models for chronic kidney were poorly reported and often developed using inappropriate methods. J Clin Epidemiol. 2013;66:268–277. doi: https://doi.org/10.1016/j.jclinepi.2012.06.020</mixed-citation><mixed-citation xml:lang="en">Collins GS, Omar O, Shanyinde M, Yu LM. A systematic review finds prediction models for chronic kidney were poorly reported and often developed using inappropriate methods. J Clin Epidemiol. 2013;66:268–277. doi: https://doi.org/10.1016/j.jclinepi.2012.06.020</mixed-citation></citation-alternatives></ref><ref id="cit54"><label>54</label><citation-alternatives><mixed-citation xml:lang="ru">Mallett S, Royston P, Dutton S, et al. Reporting methods in studies developing prognostic models in cancer: a review. BMC Med. 2010;8:20. doi: https://doi.org/10.1186/1741-7015-8-20</mixed-citation><mixed-citation xml:lang="en">Mallett S, Royston P, Dutton S, et al. Reporting methods in studies developing prognostic models in cancer: a review. BMC Med. 2010;8:20. doi: https://doi.org/10.1186/1741-7015-8-20</mixed-citation></citation-alternatives></ref><ref id="cit55"><label>55</label><citation-alternatives><mixed-citation xml:lang="ru">Mallett S, Royston P, Waters R, et al. Reporting performance of prognostic models in cancer: a review. BMC Med. 2010;8:21. doi: https://doi.org/10.1186/1741-7015-8-21</mixed-citation><mixed-citation xml:lang="en">Mallett S, Royston P, Waters R, et al. Reporting performance of prognostic models in cancer: a review. BMC Med. 2010;8:21. doi: https://doi.org/10.1186/1741-7015-8-21</mixed-citation></citation-alternatives></ref><ref id="cit56"><label>56</label><citation-alternatives><mixed-citation xml:lang="ru">Burton A, Altman DG. Missing covariate data within cancer prognostic studies: a review of current reporting and proposed guidelines. Br J Cancer. 2004;91(1):4–8. doi: https://doi.org/10.1038/sj.bjc.6601907</mixed-citation><mixed-citation xml:lang="en">Burton A, Altman DG. Missing covariate data within cancer prognostic studies: a review of current reporting and proposed guidelines. Br J Cancer. 2004;91(1):4–8. doi: https://doi.org/10.1038/sj.bjc.6601907</mixed-citation></citation-alternatives></ref><ref id="cit57"><label>57</label><citation-alternatives><mixed-citation xml:lang="ru">Concato J, Feinstein AR, Holford TR. The risk of determining risk with multivariable models. Ann Intern Med. 1993;118(3):201–210. doi: https://doi.org/10.7326/0003-4819-118-3-199302010-00009</mixed-citation><mixed-citation xml:lang="en">Concato J, Feinstein AR, Holford TR. The risk of determining risk with multivariable models. Ann Intern Med. 1993;118(3):201–210. doi: https://doi.org/10.7326/0003-4819-118-3-199302010-00009</mixed-citation></citation-alternatives></ref><ref id="cit58"><label>58</label><citation-alternatives><mixed-citation xml:lang="ru">Laupacis A, Sekar N, Stiell IG. Clinical prediction rules. A review and suggested modifications of methodological standards. JAMA. 1997;277(6):488–494.</mixed-citation><mixed-citation xml:lang="en">Laupacis A, Sekar N, Stiell IG. Clinical prediction rules. A review and suggested modifications of methodological standards. JAMA. 1997;277(6):488–494.</mixed-citation></citation-alternatives></ref><ref id="cit59"><label>59</label><citation-alternatives><mixed-citation xml:lang="ru">Steurer J, Haller C, Häuselmann H, et al. Clinical value of prognostic instruments to identify patients with an increased risk for osteoporotic fractures: systematic review. PLoS One. 2011;6(5):e19994. doi: https://doi.org/10.1371/journal.pone.0019994</mixed-citation><mixed-citation xml:lang="en">Steurer J, Haller C, Häuselmann H, et al. Clinical value of prognostic instruments to identify patients with an increased risk for osteoporotic fractures: systematic review. PLoS One. 2011;6(5):e19994. doi: https://doi.org/10.1371/journal.pone.0019994</mixed-citation></citation-alternatives></ref><ref id="cit60"><label>60</label><citation-alternatives><mixed-citation xml:lang="ru">van Dijk WD, Bemt L, Haak-Rongen S, et al. Multidimensional prognostic indices for use in COPD patient care. A systematic review. Respir Res. 2011;12(1):151. doi: https://doi.org/10.1186/1465-9921-12-151</mixed-citation><mixed-citation xml:lang="en">van Dijk WD, Bemt L, Haak-Rongen S, et al. Multidimensional prognostic indices for use in COPD patient care. A systematic review. Respir Res. 2011;12(1):151. doi: https://doi.org/10.1186/1465-9921-12-151</mixed-citation></citation-alternatives></ref><ref id="cit61"><label>61</label><citation-alternatives><mixed-citation xml:lang="ru">Hayden JA, Côté P, Bombardier C. Evaluation of the quality of prognosis studies in systematic reviews. Ann Intern Med. 2006;144(6):427–437. doi: https://doi.org/10.7326/0003-4819-144-6-200603210-00010</mixed-citation><mixed-citation xml:lang="en">Hayden JA, Côté P, Bombardier C. Evaluation of the quality of prognosis studies in systematic reviews. Ann Intern Med. 2006;144(6):427–437. doi: https://doi.org/10.7326/0003-4819-144-6-200603210-00010</mixed-citation></citation-alternatives></ref><ref id="cit62"><label>62</label><citation-alternatives><mixed-citation xml:lang="ru">Meads C, Ahmed I, Riley RD. A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance. Breast Cancer Res Treat. 2012;132(2):365–377. doi: https://doi.org/10.1007/s10549-011-1818-2</mixed-citation><mixed-citation xml:lang="en">Meads C, Ahmed I, Riley RD. A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance. Breast Cancer Res Treat. 2012;132(2):365–377. doi: https://doi.org/10.1007/s10549-011-1818-2</mixed-citation></citation-alternatives></ref><ref id="cit63"><label>63</label><citation-alternatives><mixed-citation xml:lang="ru">Mushkudiani NA, Hukkelhoven CW, Hernández AV, et al. A sys tematic review finds methodological improvements neces sary for prognostic models in determining traumatic brain injury outcomes. J Clin Epidemiol. 2008;61(4):331–343. doi: https://doi.org/10.1016/j.jclinepi.2007.06.011</mixed-citation><mixed-citation xml:lang="en">Mushkudiani NA, Hukkelhoven CW, Hernández AV, et al. A sys tematic review finds methodological improvements neces sary for prognostic models in determining traumatic brain injury outcomes. J Clin Epidemiol. 2008;61(4):331–343. doi: https://doi.org/10.1016/j.jclinepi.2007.06.011</mixed-citation></citation-alternatives></ref><ref id="cit64"><label>64</label><citation-alternatives><mixed-citation xml:lang="ru">Rehn M, Perel P, Blackhall K, Lossius HM. Prognostic models for the early care of trauma patients: a systematic review. Scand J Trauma Resusc Emerg Med. 2011;19:17. doi: https://doi.org/10.1186/1757-7241-19-17</mixed-citation><mixed-citation xml:lang="en">Rehn M, Perel P, Blackhall K, Lossius HM. Prognostic models for the early care of trauma patients: a systematic review. Scand J Trauma Resusc Emerg Med. 2011;19:17. doi: https://doi.org/10.1186/1757-7241-19-17</mixed-citation></citation-alternatives></ref><ref id="cit65"><label>65</label><citation-alternatives><mixed-citation xml:lang="ru">Siontis GC, Tzoulaki I, Ioannidis JP. Predicting death: an empirical evaluation of predictive tools for mortality. Arch Intern Med. 2011;171(19):1721–1726. doi: https://doi.org/10.1001/archinternmed.2011.334</mixed-citation><mixed-citation xml:lang="en">Siontis GC, Tzoulaki I, Ioannidis JP. Predicting death: an empirical evaluation of predictive tools for mortality. Arch Intern Med. 2011;171(19):1721–1726. doi: https://doi.org/10.1001/archinternmed.2011.334</mixed-citation></citation-alternatives></ref><ref id="cit66"><label>66</label><citation-alternatives><mixed-citation xml:lang="ru">Medlock S, Ravelli ACJ, Tamminga P, et al. Prediction of mortality in very premature infants: a systematic review of prediction models. PLoS One. 2011;6(9):e23441. doi: https://doi.org/10.1371/journal.pone.0023441</mixed-citation><mixed-citation xml:lang="en">Medlock S, Ravelli ACJ, Tamminga P, et al. Prediction of mortality in very premature infants: a systematic review of prediction models. PLoS One. 2011;6(9):e23441. doi: https://doi.org/10.1371/journal.pone.0023441</mixed-citation></citation-alternatives></ref><ref id="cit67"><label>67</label><citation-alternatives><mixed-citation xml:lang="ru">Maguire JL, Kulik DM, Laupacis A, et al. Clinical prediction rules for children: a systematic review. Pediatrics. 2011;128(3): e666–e677. doi: https://doi.org/10.1542/peds.2011-0043</mixed-citation><mixed-citation xml:lang="en">Maguire JL, Kulik DM, Laupacis A, et al. Clinical prediction rules for children: a systematic review. Pediatrics. 2011;128(3): e666–e677. doi: https://doi.org/10.1542/peds.2011-0043</mixed-citation></citation-alternatives></ref><ref id="cit68"><label>68</label><citation-alternatives><mixed-citation xml:lang="ru">Kulik DM, Uleryk EM, Maguire JL. Does this child have appendicitis? A systematic review of clinical prediction rules for children with acute abdominal pain. J Clin Epidemiol. 2013;66(1): 95–104. doi: https://doi.org/10.1016/j.jclinepi.2012.09.004</mixed-citation><mixed-citation xml:lang="en">Kulik DM, Uleryk EM, Maguire JL. Does this child have appendicitis? A systematic review of clinical prediction rules for children with acute abdominal pain. J Clin Epidemiol. 2013;66(1): 95–104. doi: https://doi.org/10.1016/j.jclinepi.2012.09.004</mixed-citation></citation-alternatives></ref><ref id="cit69"><label>69</label><citation-alternatives><mixed-citation xml:lang="ru">Kulik DM, Uleryk EM, Maguire JL. Does this child have bacterial meningitis? A systematic review of clinical prediction rules for children with suspected bacterial meningitis. J Emerg Med. 2013;45: 508–519. doi: https://doi.org/10.1016/j.jemermed.2013.03.042</mixed-citation><mixed-citation xml:lang="en">Kulik DM, Uleryk EM, Maguire JL. Does this child have bacterial meningitis? A systematic review of clinical prediction rules for children with suspected bacterial meningitis. J Emerg Med. 2013;45: 508–519. doi: https://doi.org/10.1016/j.jemermed.2013.03.042</mixed-citation></citation-alternatives></ref><ref id="cit70"><label>70</label><citation-alternatives><mixed-citation xml:lang="ru">Jacob M, Lewsey JD, Sharpin C, et al. Systematic review and validation of prognostic models in liver transplantation. Liver Transpl. 2005;11(7):814–825. doi: https://doi.org/10.1002/lt.20456</mixed-citation><mixed-citation xml:lang="en">Jacob M, Lewsey JD, Sharpin C, et al. Systematic review and validation of prognostic models in liver transplantation. Liver Transpl. 2005;11(7):814–825. doi: https://doi.org/10.1002/lt.20456</mixed-citation></citation-alternatives></ref><ref id="cit71"><label>71</label><citation-alternatives><mixed-citation xml:lang="ru">Hussain A, Choukairi F, Dunn K. Predicting survival in thermal injury: a systematic review of methodology of composite prediction models. Burns. 2013;39(5):835–850. doi: https://doi.org/10.1016/j.burns.2012.12.010</mixed-citation><mixed-citation xml:lang="en">Hussain A, Choukairi F, Dunn K. Predicting survival in thermal injury: a systematic review of methodology of composite prediction models. Burns. 2013;39(5):835–850. doi: https://doi.org/10.1016/j.burns.2012.12.010</mixed-citation></citation-alternatives></ref><ref id="cit72"><label>72</label><citation-alternatives><mixed-citation xml:lang="ru">Haskins R, Rivett DA, Osmotherly PG. Clinical prediction rules in the physiotherapy management of low back pain: a systematic review. Man Ther. 2012;17(1):9–21. doi: https://doi.org/10.1016/j.math.2011.05.001</mixed-citation><mixed-citation xml:lang="en">Haskins R, Rivett DA, Osmotherly PG. Clinical prediction rules in the physiotherapy management of low back pain: a systematic review. Man Ther. 2012;17(1):9–21. doi: https://doi.org/10.1016/j.math.2011.05.001</mixed-citation></citation-alternatives></ref><ref id="cit73"><label>73</label><citation-alternatives><mixed-citation xml:lang="ru">Echouffo-Tcheugui JB, Kengne AP. Risk models to predict chronic kidney disease and its progression: a systematic review. PLoS Med. 2012;9(11):e1001344. doi: https://doi.org/10.1371/journal.pmed.1001344</mixed-citation><mixed-citation xml:lang="en">Echouffo-Tcheugui JB, Kengne AP. Risk models to predict chronic kidney disease and its progression: a systematic review. PLoS Med. 2012;9(11):e1001344. doi: https://doi.org/10.1371/journal.pmed.1001344</mixed-citation></citation-alternatives></ref><ref id="cit74"><label>74</label><citation-alternatives><mixed-citation xml:lang="ru">Echouffo-Tcheugui JB, Batty GD, Kivimäki M, Kengne AP. Risk models to predict hypertension: a systematic review. PLoS One. 2013; 8(7):e67370. doi: https://doi.org/10.1371/journal.pone.0067370</mixed-citation><mixed-citation xml:lang="en">Echouffo-Tcheugui JB, Batty GD, Kivimäki M, Kengne AP. Risk models to predict hypertension: a systematic review. PLoS One. 2013; 8(7):e67370. doi: https://doi.org/10.1371/journal.pone.0067370</mixed-citation></citation-alternatives></ref><ref id="cit75"><label>75</label><citation-alternatives><mixed-citation xml:lang="ru">Anothaisintawee T, Teerawattananon Y, Wiratkapun C, et al. Risk prediction models of breast cancer: a systematic review of model performances. Breast Cancer Res Treat. 2012;133(1):1–10. doi: https://doi.org/10.1007/s10549-011-1853-z</mixed-citation><mixed-citation xml:lang="en">Anothaisintawee T, Teerawattananon Y, Wiratkapun C, et al. Risk prediction models of breast cancer: a systematic review of model performances. Breast Cancer Res Treat. 2012;133(1):1–10. doi: https://doi.org/10.1007/s10549-011-1853-z</mixed-citation></citation-alternatives></ref><ref id="cit76"><label>76</label><citation-alternatives><mixed-citation xml:lang="ru">van Oort L, van den Berg T, Koes BW, et al. Preliminary state of development of prediction models for primary care physical therapy: a systematic review. J Clin Epidemiol. 2012;65(12):1257–1266. doi: https://doi.org/10.1016/j.jclinepi.2012.05.007</mixed-citation><mixed-citation xml:lang="en">van Oort L, van den Berg T, Koes BW, et al. Preliminary state of development of prediction models for primary care physical therapy: a systematic review. J Clin Epidemiol. 2012;65(12):1257–1266. doi: https://doi.org/10.1016/j.jclinepi.2012.05.007</mixed-citation></citation-alternatives></ref><ref id="cit77"><label>77</label><citation-alternatives><mixed-citation xml:lang="ru">Tangri N, Kitsios GD, Inker LA, et al. Risk prediction models for patients with chronic kidney disease: a systematic review. Ann Intern Med. 2013;158(8):596–603. doi: https://doi.org/10.7326/0003-4819-158-8-201304160-00004</mixed-citation><mixed-citation xml:lang="en">Tangri N, Kitsios GD, Inker LA, et al. Risk prediction models for patients with chronic kidney disease: a systematic review. Ann Intern Med. 2013;158(8):596–603. doi: https://doi.org/10.7326/0003-4819-158-8-201304160-00004</mixed-citation></citation-alternatives></ref><ref id="cit78"><label>78</label><citation-alternatives><mixed-citation xml:lang="ru">van Hanegem N, Breijer MC, Opmeer BC, et al. Prediction models in women with postmenopausal bleeding: a systematic review. Womens Health (Lond Engl). 2012;8(3):251–262. doi: https://doi.org/10.2217/whe.12.10</mixed-citation><mixed-citation xml:lang="en">van Hanegem N, Breijer MC, Opmeer BC, et al. Prediction models in women with postmenopausal bleeding: a systematic review. Womens Health (Lond Engl). 2012;8(3):251–262. doi: https://doi.org/10.2217/whe.12.10</mixed-citation></citation-alternatives></ref><ref id="cit79"><label>79</label><citation-alternatives><mixed-citation xml:lang="ru">Minne L, Ludikhuize J, de Jonge E, et al. Prognostic models for predicting mortality in elderly ICU patients: a systematic review. Intensive Care Med. 2011;37(8):1258–1268. doi: https://doi.org/10.1007/s00134-011-2265-6</mixed-citation><mixed-citation xml:lang="en">Minne L, Ludikhuize J, de Jonge E, et al. Prognostic models for predicting mortality in elderly ICU patients: a systematic review. Intensive Care Med. 2011;37(8):1258–1268. doi: https://doi.org/10.1007/s00134-011-2265-6</mixed-citation></citation-alternatives></ref><ref id="cit80"><label>80</label><citation-alternatives><mixed-citation xml:lang="ru">Leushuis E, van der Steeg JW, Steures P, et al. Prediction models in reproductive medicine: a critical appraisal. Hum Reprod Update. 2009;15(5):537–552. doi: https://doi.org/10.1093/humupd/dmp013</mixed-citation><mixed-citation xml:lang="en">Leushuis E, van der Steeg JW, Steures P, et al. Prediction models in reproductive medicine: a critical appraisal. Hum Reprod Update. 2009;15(5):537–552. doi: https://doi.org/10.1093/humupd/dmp013</mixed-citation></citation-alternatives></ref><ref id="cit81"><label>81</label><citation-alternatives><mixed-citation xml:lang="ru">Jaja BN, Cusimano MD, Etminan N, et al. Clinical prediction models for aneurysmal subarachnoid hemorrhage: a systematic review. Neurocrit Care. 2013;18(1):143–153. doi: https://doi.org/10.1007/s12028-012-9792-z</mixed-citation><mixed-citation xml:lang="en">Jaja BN, Cusimano MD, Etminan N, et al. Clinical prediction models for aneurysmal subarachnoid hemorrhage: a systematic review. Neurocrit Care. 2013;18(1):143–153. doi: https://doi.org/10.1007/s12028-012-9792-z</mixed-citation></citation-alternatives></ref><ref id="cit82"><label>82</label><citation-alternatives><mixed-citation xml:lang="ru">Wlodzimirow KA, Eslami S, Chamuleau RA, et al. Prediction of poor outcome in patients with acute liver failure-systematic review of prediction models. PLoS One. 2012;7(12):e50952. doi: https://doi.org/10.1371/journal.pone.0050952</mixed-citation><mixed-citation xml:lang="en">Wlodzimirow KA, Eslami S, Chamuleau RA, et al. Prediction of poor outcome in patients with acute liver failure-systematic review of prediction models. PLoS One. 2012;7(12):e50952. doi: https://doi.org/10.1371/journal.pone.0050952</mixed-citation></citation-alternatives></ref><ref id="cit83"><label>83</label><citation-alternatives><mixed-citation xml:lang="ru">Phillips B, Wade R, Stewart LA, Sutton AJ. Systematic review and meta-analysis of the discriminatory performance of risk prediction rules in febrile neutropaenic episodes in children and young people. Eur J Cancer. 2010;46(16):2950–2964. doi: https://doi.org/10.1016/j.ejca.2010.05.024</mixed-citation><mixed-citation xml:lang="en">Phillips B, Wade R, Stewart LA, Sutton AJ. Systematic review and meta-analysis of the discriminatory performance of risk prediction rules in febrile neutropaenic episodes in children and young people. Eur J Cancer. 2010;46(16):2950–2964. doi: https://doi.org/10.1016/j.ejca.2010.05.024</mixed-citation></citation-alternatives></ref><ref id="cit84"><label>84</label><citation-alternatives><mixed-citation xml:lang="ru">Rubin KH, Friis-Holmberg T, Hermann AP, et al. Risk assessment tools to identify women with increased risk of osteoporotic fracture: complexity or simplicity? A systematic review. J Bone Miner Res. 2013;28(8):1701–1717. doi: https://doi.org/10.1002/jbmr.1956</mixed-citation><mixed-citation xml:lang="en">Rubin KH, Friis-Holmberg T, Hermann AP, et al. Risk assessment tools to identify women with increased risk of osteoporotic fracture: complexity or simplicity? A systematic review. J Bone Miner Res. 2013;28(8):1701–1717. doi: https://doi.org/10.1002/jbmr.1956</mixed-citation></citation-alternatives></ref><ref id="cit85"><label>85</label><citation-alternatives><mixed-citation xml:lang="ru">Abbasi A, Peelen LM, Corpeleijn E, et al. Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study. BMJ. 2012;345:e5900. doi: https://doi.org/10.1136/bmj.e5900</mixed-citation><mixed-citation xml:lang="en">Abbasi A, Peelen LM, Corpeleijn E, et al. Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study. BMJ. 2012;345:e5900. doi: https://doi.org/10.1136/bmj.e5900</mixed-citation></citation-alternatives></ref><ref id="cit86"><label>86</label><citation-alternatives><mixed-citation xml:lang="ru">Braband M, Folkestad L, Clausen NG, et al. Risk scoring systems for adults admitted to the emergency department: a systematic review. Scand J Trauma Resusc Emerg Med. 2010;18:8. doi: https://doi.org/10.1186/1757-7241-18-8</mixed-citation><mixed-citation xml:lang="en">Braband M, Folkestad L, Clausen NG, et al. Risk scoring systems for adults admitted to the emergency department: a systematic review. Scand J Trauma Resusc Emerg Med. 2010;18:8. doi: https://doi.org/10.1186/1757-7241-18-8</mixed-citation></citation-alternatives></ref><ref id="cit87"><label>87</label><citation-alternatives><mixed-citation xml:lang="ru">Maguire JL, Boutis K, Uleryk EM, et al. Should a head-injured child receive a head CT scan? A systematic review of clinical prediction rules. Pediatrics. 2009;124(1):e145–e154. doi: https://doi.org/10.1542/peds.2009-0075</mixed-citation><mixed-citation xml:lang="en">Maguire JL, Boutis K, Uleryk EM, et al. Should a head-injured child receive a head CT scan? A systematic review of clinical prediction rules. Pediatrics. 2009;124(1):e145–e154. doi: https://doi.org/10.1542/peds.2009-0075</mixed-citation></citation-alternatives></ref><ref id="cit88"><label>88</label><citation-alternatives><mixed-citation xml:lang="ru">Vuong K, McGeechan K, Armstrong BK, Cust AE. Risk prediction models for incident primary cutaneous melanoma: a systematic review. JAMA Dermatol. 2014;150(4):434–444. doi: https://doi.org/10.1001/jamadermatol.2013.8890</mixed-citation><mixed-citation xml:lang="en">Vuong K, McGeechan K, Armstrong BK, Cust AE. Risk prediction models for incident primary cutaneous melanoma: a systematic review. JAMA Dermatol. 2014;150(4):434–444. doi: https://doi.org/10.1001/jamadermatol.2013.8890</mixed-citation></citation-alternatives></ref><ref id="cit89"><label>89</label><citation-alternatives><mixed-citation xml:lang="ru">Ahmed I, Debray TP, Moons KG, Riley RD. Developing and validating risk prediction models in an individual participant data meta-analysis. BMC Med Res Methodol. 2014;14:3. doi: https://doi.org/10.1186/1471-2288-14-3</mixed-citation><mixed-citation xml:lang="en">Ahmed I, Debray TP, Moons KG, Riley RD. Developing and validating risk prediction models in an individual participant data meta-analysis. BMC Med Res Methodol. 2014;14:3. doi: https://doi.org/10.1186/1471-2288-14-3</mixed-citation></citation-alternatives></ref><ref id="cit90"><label>90</label><citation-alternatives><mixed-citation xml:lang="ru">Huen SC, Parikh CR. Predicting acute kidney injury after cardiac surgery: a systematic review. Ann Thorac Surg. 2012;93(1): 337–341. doi: https://doi.org/10.1016/j.athoracsur.2011.09.010</mixed-citation><mixed-citation xml:lang="en">Huen SC, Parikh CR. Predicting acute kidney injury after cardiac surgery: a systematic review. Ann Thorac Surg. 2012;93(1): 337–341. doi: https://doi.org/10.1016/j.athoracsur.2011.09.010</mixed-citation></citation-alternatives></ref><ref id="cit91"><label>91</label><citation-alternatives><mixed-citation xml:lang="ru">Calle P, Cerro L, Valencia J, Jaimes F. Usefulness of severity scores in patients with suspected infection in the emergency department: a systematic review. J Emerg Med. 2012;42(4): 379–391. doi: https://doi.org/10.1016/j.jemermed.2011.03.033</mixed-citation><mixed-citation xml:lang="en">Calle P, Cerro L, Valencia J, Jaimes F. Usefulness of severity scores in patients with suspected infection in the emergency department: a systematic review. J Emerg Med. 2012;42(4): 379–391. doi: https://doi.org/10.1016/j.jemermed.2011.03.033</mixed-citation></citation-alternatives></ref><ref id="cit92"><label>92</label><citation-alternatives><mixed-citation xml:lang="ru">Usher-Smith JA, Emery J, Kassianos AP, Walter FM. Risk prediction models for melanoma: a systematic review. Cancer Epidemiol Biomarkers Prev. 2014;23(8):1450–1463. doi: https://doi.org/10.1158/1055-9965.EPI-14-0295</mixed-citation><mixed-citation xml:lang="en">Usher-Smith JA, Emery J, Kassianos AP, Walter FM. Risk prediction models for melanoma: a systematic review. Cancer Epidemiol Biomarkers Prev. 2014;23(8):1450–1463. doi: https://doi.org/10.1158/1055-9965.EPI-14-0295</mixed-citation></citation-alternatives></ref><ref id="cit93"><label>93</label><citation-alternatives><mixed-citation xml:lang="ru">Warnell I, Chincholkar M, Eccles M. Predicting perioperative mortality after oesophagectomy: a systematic review of performance and methods of multivariate models. Br J Anaesth. 2015;114(1): 32–43. doi: https://doi.org/10.1093/bja/aeu294</mixed-citation><mixed-citation xml:lang="en">Warnell I, Chincholkar M, Eccles M. Predicting perioperative mortality after oesophagectomy: a systematic review of performance and methods of multivariate models. Br J Anaesth. 2015;114(1): 32–43. doi: https://doi.org/10.1093/bja/aeu294</mixed-citation></citation-alternatives></ref><ref id="cit94"><label>94</label><citation-alternatives><mixed-citation xml:lang="ru">Silverberg N, Gardner AJ, Brubacher J, et al. Systematic review of multivariable prognostic models for mild traumatic brain injury. J Neurotrauma. 2015;32(8):517–526. doi: https://doi.org/10.1089/neu.2014.3600</mixed-citation><mixed-citation xml:lang="en">Silverberg N, Gardner AJ, Brubacher J, et al. Systematic review of multivariable prognostic models for mild traumatic brain injury. J Neurotrauma. 2015;32(8):517–526. doi: https://doi.org/10.1089/neu.2014.3600</mixed-citation></citation-alternatives></ref><ref id="cit95"><label>95</label><citation-alternatives><mixed-citation xml:lang="ru">Delebarre M, Macher E, Mazingue F, et al. Which decision rules meet methodological standards in children with febrile neutropenia? Results of a systematic review and analysis. Pediatr Blood Cancer. 2014; 61(10):1786–1791. doi: https://doi.org/10.1002/pbc.25106</mixed-citation><mixed-citation xml:lang="en">Delebarre M, Macher E, Mazingue F, et al. Which decision rules meet methodological standards in children with febrile neutropenia? Results of a systematic review and analysis. Pediatr Blood Cancer. 2014; 61(10):1786–1791. doi: https://doi.org/10.1002/pbc.25106</mixed-citation></citation-alternatives></ref><ref id="cit96"><label>96</label><citation-alternatives><mixed-citation xml:lang="ru">Schulz KF, Altman DG, Moher D. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ. 2010;340:c332. doi: https://doi.org/10.1136/bmj.c332</mixed-citation><mixed-citation xml:lang="en">Schulz KF, Altman DG, Moher D. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ. 2010;340:c332. doi: https://doi.org/10.1136/bmj.c332</mixed-citation></citation-alternatives></ref><ref id="cit97"><label>97</label><citation-alternatives><mixed-citation xml:lang="ru">von Elm E, Altman DG, Egger M, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ. 2007;335(7624): 806–808. doi: https://doi.org/10.1136/bmj.39335.541782.AD</mixed-citation><mixed-citation xml:lang="en">von Elm E, Altman DG, Egger M, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ. 2007;335(7624): 806–808. doi: https://doi.org/10.1136/bmj.39335.541782.AD</mixed-citation></citation-alternatives></ref><ref id="cit98"><label>98</label><citation-alternatives><mixed-citation xml:lang="ru">McShane LM, Altman DG, Sauerbrei W, et al. Reporting recom mendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst. 2005;97(16):1180–1184. doi: https://doi.org/10.1093/jnci/dji237</mixed-citation><mixed-citation xml:lang="en">McShane LM, Altman DG, Sauerbrei W, et al. Reporting recom mendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst. 2005;97(16):1180–1184. doi: https://doi.org/10.1093/jnci/dji237</mixed-citation></citation-alternatives></ref><ref id="cit99"><label>99</label><citation-alternatives><mixed-citation xml:lang="ru">Gallo V, Egger M, McCormack V, et al. STrengthening the Reporting of OBservational studies in Epidemiology — Molecular Epidemiology (STROBE-ME): an extension of the STROBE statement. Eur J Clin Invest. 2012;42(1):1–16. doi: https://doi.org/10.1111/j.1365-2362.2011.02561.x</mixed-citation><mixed-citation xml:lang="en">Gallo V, Egger M, McCormack V, et al. STrengthening the Reporting of OBservational studies in Epidemiology — Molecular Epidemiology (STROBE-ME): an extension of the STROBE statement. Eur J Clin Invest. 2012;42(1):1–16. doi: https://doi.org/10.1111/j.1365-2362.2011.02561.x</mixed-citation></citation-alternatives></ref><ref id="cit100"><label>100</label><citation-alternatives><mixed-citation xml:lang="ru">Bossuyt PM, Reitsma JB, Bruns DE, et al. Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD Initiative. Radiology. 2003;226:575–580. doi: https://doi.org/10.1016/S0009-9260(03)00258-7</mixed-citation><mixed-citation xml:lang="en">Bossuyt PM, Reitsma JB, Bruns DE, et al. Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD Initiative. Radiology. 2003;226:575–580. doi: https://doi.org/10.1016/S0009-9260(03)00258-7</mixed-citation></citation-alternatives></ref><ref id="cit101"><label>101</label><citation-alternatives><mixed-citation xml:lang="ru">Janssens AC, Ioannidis JP, vanDuijn CM, et al. Strengthening the reporting of genetic risk prediction studies: the GRIPS statement. Eur J Clin Invest. 2011;41(9):1004–1009. doi: https://doi.org/10.1111/j.1365-2362.2011.02494.x</mixed-citation><mixed-citation xml:lang="en">Janssens AC, Ioannidis JP, vanDuijn CM, et al. Strengthening the reporting of genetic risk prediction studies: the GRIPS statement. Eur J Clin Invest. 2011;41(9):1004–1009. doi: https://doi.org/10.1111/j.1365-2362.2011.02494.x</mixed-citation></citation-alternatives></ref><ref id="cit102"><label>102</label><citation-alternatives><mixed-citation xml:lang="ru">Moons KG, Altman DG, Vergouwe Y, Royston P. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ. 2009;338:b606. doi: https://doi.org/10.1136/bmj.b606</mixed-citation><mixed-citation xml:lang="en">Moons KG, Altman DG, Vergouwe Y, Royston P. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ. 2009;338:b606. doi: https://doi.org/10.1136/bmj.b606</mixed-citation></citation-alternatives></ref><ref id="cit103"><label>103</label><citation-alternatives><mixed-citation xml:lang="ru">Moons KG, Kengne AP, Woodward M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio) marker. Heart. 2012;98(9): 683–690. doi: https://doi.org/10.1136/heartjnl-2011-301246</mixed-citation><mixed-citation xml:lang="en">Moons KG, Kengne AP, Woodward M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio) marker. Heart. 2012;98(9): 683–690. doi: https://doi.org/10.1136/heartjnl-2011-301246</mixed-citation></citation-alternatives></ref><ref id="cit104"><label>104</label><citation-alternatives><mixed-citation xml:lang="ru">Labarère J, Bertrand R, Fine MJ. How to derive and validate clinical prediction models for use in intensive care medicine. Intensive Care Med. 2014;40(4):513–527. doi: https://doi.org/10.1007/s00134-014-3227-6</mixed-citation><mixed-citation xml:lang="en">Labarère J, Bertrand R, Fine MJ. How to derive and validate clinical prediction models for use in intensive care medicine. Intensive Care Med. 2014;40(4):513–527. doi: https://doi.org/10.1007/s00134-014-3227-6</mixed-citation></citation-alternatives></ref><ref id="cit105"><label>105</label><citation-alternatives><mixed-citation xml:lang="ru">Tzoulaki I, Liberopoulos G, Ioannidis JP. Use of reclassification for assessment of improved prediction: an empirical evaluation. Int J Epidemiol. 2011;40(4):1094–1105. doi: https://doi.org/10.1093/ije/dyr013</mixed-citation><mixed-citation xml:lang="en">Tzoulaki I, Liberopoulos G, Ioannidis JP. Use of reclassification for assessment of improved prediction: an empirical evaluation. Int J Epidemiol. 2011;40(4):1094–1105. doi: https://doi.org/10.1093/ije/dyr013</mixed-citation></citation-alternatives></ref><ref id="cit106"><label>106</label><citation-alternatives><mixed-citation xml:lang="ru">Peters SA, Bakker M, den Ruijter HM, Bots ML. Added value of CAC in risk stratification for cardiovascular events: a systematic review. Eur J Clin Invest. 2012;42(1):110–116. doi: https://doi.org/10.1111/j.1365-2362.2011.02555.x</mixed-citation><mixed-citation xml:lang="en">Peters SA, Bakker M, den Ruijter HM, Bots ML. Added value of CAC in risk stratification for cardiovascular events: a systematic review. Eur J Clin Invest. 2012;42(1):110–116. doi: https://doi.org/10.1111/j.1365-2362.2011.02555.x</mixed-citation></citation-alternatives></ref><ref id="cit107"><label>107</label><citation-alternatives><mixed-citation xml:lang="ru">Wallace E, Smith SM, Perera-Salazar R, et al. Framework for the impact analysis and implementation of clinical prediction rules (CPRs). BMC Med Inform Decis Mak. 2011;11:62. doi: https://doi.org/10.1186/1472-6947-11-62</mixed-citation><mixed-citation xml:lang="en">Wallace E, Smith SM, Perera-Salazar R, et al. Framework for the impact analysis and implementation of clinical prediction rules (CPRs). BMC Med Inform Decis Mak. 2011;11:62. doi: https://doi.org/10.1186/1472-6947-11-62</mixed-citation></citation-alternatives></ref><ref id="cit108"><label>108</label><citation-alternatives><mixed-citation xml:lang="ru">Altman DG, McShane LM, Sauerbrei W, Taube SE. Reporting recommendations for tumor marker prognostic studies (REMARK): explanation and elaboration. BMC Med. 2012;10:51. doi: https://doi.org/10.1186/1741-7015-10-51</mixed-citation><mixed-citation xml:lang="en">Altman DG, McShane LM, Sauerbrei W, Taube SE. Reporting recommendations for tumor marker prognostic studies (REMARK): explanation and elaboration. BMC Med. 2012;10:51. doi: https://doi.org/10.1186/1741-7015-10-51</mixed-citation></citation-alternatives></ref><ref id="cit109"><label>109</label><citation-alternatives><mixed-citation xml:lang="ru">Campbell MK, Elbourne DR, Altman DG. CONSORT statement: extension to cluster randomised trials. BMJ. 2004;328(7441): 702–708. doi: https://doi.org/10.1136/bmj.328.7441.702</mixed-citation><mixed-citation xml:lang="en">Campbell MK, Elbourne DR, Altman DG. CONSORT statement: extension to cluster randomised trials. BMJ. 2004;328(7441): 702–708. doi: https://doi.org/10.1136/bmj.328.7441.702</mixed-citation></citation-alternatives></ref><ref id="cit110"><label>110</label><citation-alternatives><mixed-citation xml:lang="ru">Teasdale G, Jennett B. Assessment of coma and impaired consciousness. A practical scale. Lancet. 1974;2(7872):81–84. doi: https://doi.org/10.1016/s0140-6736(74)91639-0</mixed-citation><mixed-citation xml:lang="en">Teasdale G, Jennett B. Assessment of coma and impaired consciousness. A practical scale. Lancet. 1974;2(7872):81–84. doi: https://doi.org/10.1016/s0140-6736(74)91639-0</mixed-citation></citation-alternatives></ref><ref id="cit111"><label>111</label><citation-alternatives><mixed-citation xml:lang="ru">Farrell B, Godwin J, Richards S, Warlow C. The United Kingdom transient ischaemic attack (UK-TIA) aspirin trial: final results. J Neurol Neurosurg Psychiatry. 1991;54(12):1044–1054. doi: https://doi.org/10.1136/jnnp.54.12.1044</mixed-citation><mixed-citation xml:lang="en">Farrell B, Godwin J, Richards S, Warlow C. The United Kingdom transient ischaemic attack (UK-TIA) aspirin trial: final results. J Neurol Neurosurg Psychiatry. 1991;54(12):1044–1054. doi: https://doi.org/10.1136/jnnp.54.12.1044</mixed-citation></citation-alternatives></ref><ref id="cit112"><label>112</label><citation-alternatives><mixed-citation xml:lang="ru">Harrell FE. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression and Survival Analysis. New York: Springer; 2001.</mixed-citation><mixed-citation xml:lang="en">Harrell FE. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression and Survival Analysis. New York: Springer; 2001.</mixed-citation></citation-alternatives></ref><ref id="cit113"><label>113</label><citation-alternatives><mixed-citation xml:lang="ru">Moher D, Schulz KF, Simera I, Altman DG. Guidance for developers of health research reporting guidelines. PLoS Med. 2010;7(2): e1000217. doi: https://doi.org/10.1371/journal.pmed.1000217</mixed-citation><mixed-citation xml:lang="en">Moher D, Schulz KF, Simera I, Altman DG. Guidance for developers of health research reporting guidelines. PLoS Med. 2010;7(2): e1000217. doi: https://doi.org/10.1371/journal.pmed.1000217</mixed-citation></citation-alternatives></ref><ref id="cit114"><label>114</label><citation-alternatives><mixed-citation xml:lang="ru">Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis: the TRIPOD statement. Ann Intern Med. 2015;162(1):55–63. doi: https://doi.org/10.7326/M14-0697</mixed-citation><mixed-citation xml:lang="en">Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis: the TRIPOD statement. Ann Intern Med. 2015;162(1):55–63. doi: https://doi.org/10.7326/M14-0697</mixed-citation></citation-alternatives></ref><ref id="cit115"><label>115</label><citation-alternatives><mixed-citation xml:lang="ru">Morise AP, Haddad WJ, Beckner D. Development and validation of a clinical score to estimate the probability of coronary artery disease in men and women presenting with suspected coronary disease. Am J Med. 1997;102(4):350–356. doi: https://doi.org/10.1016/s0002-9343(97)00086-7</mixed-citation><mixed-citation xml:lang="en">Morise AP, Haddad WJ, Beckner D. Development and validation of a clinical score to estimate the probability of coronary artery disease in men and women presenting with suspected coronary disease. Am J Med. 1997;102(4):350–356. doi: https://doi.org/10.1016/s0002-9343(97)00086-7</mixed-citation></citation-alternatives></ref><ref id="cit116"><label>116</label><citation-alternatives><mixed-citation xml:lang="ru">Dehing-Oberije C, Yu S, DeRuysscher D, et al. Development and external validation of prognostic model for 2-year survival of non-small-cell lung cancer patients treated with chemoradiotherapy. Int J Radiat Oncol Biol Phys. 2009;74(2):355–362. doi: https://doi.org/10.1016/j.ijrobp.2008.08.052</mixed-citation><mixed-citation xml:lang="en">Dehing-Oberije C, Yu S, DeRuysscher D, et al. Development and external validation of prognostic model for 2-year survival of non-small-cell lung cancer patients treated with chemoradiotherapy. Int J Radiat Oncol Biol Phys. 2009;74(2):355–362. doi: https://doi.org/10.1016/j.ijrobp.2008.08.052</mixed-citation></citation-alternatives></ref><ref id="cit117"><label>117</label><citation-alternatives><mixed-citation xml:lang="ru">Collins GS, Altman DG. Predicting the 10 year risk of cardiovascular disease in the United Kingdom: independent and external validation of an updated version of QRISK2. BMJ. 2012;344:e4181. doi: https://doi.org/10.1136/bmj.e4181</mixed-citation><mixed-citation xml:lang="en">Collins GS, Altman DG. Predicting the 10 year risk of cardiovascular disease in the United Kingdom: independent and external validation of an updated version of QRISK2. BMJ. 2012;344:e4181. doi: https://doi.org/10.1136/bmj.e4181</mixed-citation></citation-alternatives></ref><ref id="cit118"><label>118</label><citation-alternatives><mixed-citation xml:lang="ru">Michikawa T, Inoue M, Sawada N, et al. Development of a prediction model for 10-year risk of hepatocellular carcinoma in middle-aged Japanese: the Japan Public Health Center-based Prospective Study Cohort II. Prev Med. 2012;55(2):137–143. doi: https://doi.org/10.1016/j.ypmed.2012.05.017</mixed-citation><mixed-citation xml:lang="en">Michikawa T, Inoue M, Sawada N, et al. Development of a prediction model for 10-year risk of hepatocellular carcinoma in middle-aged Japanese: the Japan Public Health Center-based Prospective Study Cohort II. Prev Med. 2012;55(2):137–143. doi: https://doi.org/10.1016/j.ypmed.2012.05.017</mixed-citation></citation-alternatives></ref><ref id="cit119"><label>119</label><citation-alternatives><mixed-citation xml:lang="ru">Morise AP, Detrano R, Bobbio M, Diamond GA. Development and validation of a logistic regression-derived algorithm for estimating the incremental probability of coronary artery disease before and after exercise testing. J Am Coll Cardiol. 1992;20(5):1187–1196. doi: https://doi.org/10.1016/0735-1097(92)90377-y</mixed-citation><mixed-citation xml:lang="en">Morise AP, Detrano R, Bobbio M, Diamond GA. Development and validation of a logistic regression-derived algorithm for estimating the incremental probability of coronary artery disease before and after exercise testing. J Am Coll Cardiol. 1992;20(5):1187–1196. doi: https://doi.org/10.1016/0735-1097(92)90377-y</mixed-citation></citation-alternatives></ref><ref id="cit120"><label>120</label><citation-alternatives><mixed-citation xml:lang="ru">D’Agostino RB, Grundy S, Sullivan LM, Wilson P. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA. 2001;286:180–187. doi: https://doi.org/10.1001/jama.286.2.180</mixed-citation><mixed-citation xml:lang="en">D’Agostino RB, Grundy S, Sullivan LM, Wilson P. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA. 2001;286:180–187. doi: https://doi.org/10.1001/jama.286.2.180</mixed-citation></citation-alternatives></ref><ref id="cit121"><label>121</label><citation-alternatives><mixed-citation xml:lang="ru">Beck DH, Smith GB, Pappachan JV, Millar B. External validation of the SAPS II, APACHE II and APACHE III prognostic models in South England: a multicentre study. Intensive Care Med. 2003;29(2): 249–526. doi: https://doi.org/10.1007/s00134-002-1607-9</mixed-citation><mixed-citation xml:lang="en">Beck DH, Smith GB, Pappachan JV, Millar B. External validation of the SAPS II, APACHE II and APACHE III prognostic models in South England: a multicentre study. Intensive Care Med. 2003;29(2): 249–526. doi: https://doi.org/10.1007/s00134-002-1607-9</mixed-citation></citation-alternatives></ref><ref id="cit122"><label>122</label><citation-alternatives><mixed-citation xml:lang="ru">Collins GS, de Groot JA, Dutton S, et al. External validation of multivariable prediction models: a systematic review of methodological conduct and reporting. BMC Med Res Methodol. 2014;14:40. doi: https://doi.org/10.1186/1471-2288-14-40</mixed-citation><mixed-citation xml:lang="en">Collins GS, de Groot JA, Dutton S, et al. External validation of multivariable prediction models: a systematic review of methodological conduct and reporting. BMC Med Res Methodol. 2014;14:40. doi: https://doi.org/10.1186/1471-2288-14-40</mixed-citation></citation-alternatives></ref><ref id="cit123"><label>123</label><citation-alternatives><mixed-citation xml:lang="ru">Perel P, Prieto-Merino D, Shakur H, et al. Predicting early death in patients with traumatic bleeding: development and validation of prognostic model. BMJ. 2012;345:e5166. doi: https://doi.org/10.1136/bmj.e5166</mixed-citation><mixed-citation xml:lang="en">Perel P, Prieto-Merino D, Shakur H, et al. Predicting early death in patients with traumatic bleeding: development and validation of prognostic model. BMJ. 2012;345:e5166. doi: https://doi.org/10.1136/bmj.e5166</mixed-citation></citation-alternatives></ref><ref id="cit124"><label>124</label><citation-alternatives><mixed-citation xml:lang="ru">Stiell IG, Greenberg GH, McKnight RD, et al. Decision rules for the use of radiography in acute ankle injuries. Refinement and prospective validation. JAMA. 1993;269(9):1127–1132. doi: https://doi.org/10.1001/jama.269.9.1127</mixed-citation><mixed-citation xml:lang="en">Stiell IG, Greenberg GH, McKnight RD, et al. Decision rules for the use of radiography in acute ankle injuries. Refinement and prospective validation. JAMA. 1993;269(9):1127–1132. doi: https://doi.org/10.1001/jama.269.9.1127</mixed-citation></citation-alternatives></ref><ref id="cit125"><label>125</label><citation-alternatives><mixed-citation xml:lang="ru">Holland JL, Wilczynski NL, Haynes RB. Optimal search strategies for identifying sound clinical prediction studies in EMBASE. BMC Med Inform Decis Mak. 2005;5:11. doi: https://doi.org/10.1186/1472-6947-5-11</mixed-citation><mixed-citation xml:lang="en">Holland JL, Wilczynski NL, Haynes RB. Optimal search strategies for identifying sound clinical prediction studies in EMBASE. BMC Med Inform Decis Mak. 2005;5:11. doi: https://doi.org/10.1186/1472-6947-5-11</mixed-citation></citation-alternatives></ref><ref id="cit126"><label>126</label><citation-alternatives><mixed-citation xml:lang="ru">Ingui BJ, Rogers MA. Searching for clinical prediction rules in MEDLINE. J Am Med Inform Assoc. 2001;8(4):391–397. doi: https://doi.org/10.1136/jamia.2001.0080391</mixed-citation><mixed-citation xml:lang="en">Ingui BJ, Rogers MA. Searching for clinical prediction rules in MEDLINE. J Am Med Inform Assoc. 2001;8(4):391–397. doi: https://doi.org/10.1136/jamia.2001.0080391</mixed-citation></citation-alternatives></ref><ref id="cit127"><label>127</label><citation-alternatives><mixed-citation xml:lang="ru">Wong SS, Wilczynski NL, Haynes RB, Ramkissoonsingh R. Developing optimal search strategies for detecting sound clinical prediction studies in MEDLINE. AMIA Annu Symp Proc. 2003; 2003:728–732.</mixed-citation><mixed-citation xml:lang="en">Wong SS, Wilczynski NL, Haynes RB, Ramkissoonsingh R. Developing optimal search strategies for detecting sound clinical prediction studies in MEDLINE. AMIA Annu Symp Proc. 2003; 2003:728–732.</mixed-citation></citation-alternatives></ref><ref id="cit128"><label>128</label><citation-alternatives><mixed-citation xml:lang="ru">Geersing GJ, Bouwmeester W, Zuithoff P, et al. Search filters for finding prognostic and diagnostic prediction studies in Medline to enhance systematic reviews. PLoS One. 2012;7(2):e32844. doi: https://doi.org/10.1371/journal.pone.0032844</mixed-citation><mixed-citation xml:lang="en">Geersing GJ, Bouwmeester W, Zuithoff P, et al. Search filters for finding prognostic and diagnostic prediction studies in Medline to enhance systematic reviews. PLoS One. 2012;7(2):e32844. doi: https://doi.org/10.1371/journal.pone.0032844</mixed-citation></citation-alternatives></ref><ref id="cit129"><label>129</label><citation-alternatives><mixed-citation xml:lang="ru">Keogh C, Wallace E, O’Brien KK, et al. Optimized retrieval of primary care clinical prediction rules from to establish a Web-based register. J Clin Epidemiol. 2011;64(8):848–860. doi: https://doi.org/10.1016/j.jclinepi.2010.11.011</mixed-citation><mixed-citation xml:lang="en">Keogh C, Wallace E, O’Brien KK, et al. Optimized retrieval of primary care clinical prediction rules from to establish a Web-based register. J Clin Epidemiol. 2011;64(8):848–860. doi: https://doi.org/10.1016/j.jclinepi.2010.11.011</mixed-citation></citation-alternatives></ref><ref id="cit130"><label>130</label><citation-alternatives><mixed-citation xml:lang="ru">Rietveld RP, terRiet G, Bindels PJ, et al. Predicting bacterial cause in infectious conjunctivitis: cohort study on informativeness of combinations of signs and symptoms. BMJ. 2004;329(7459): 206–210. doi: https://doi.org/10.1136/bmj.38128.631319.AE</mixed-citation><mixed-citation xml:lang="en">Rietveld RP, terRiet G, Bindels PJ, et al. Predicting bacterial cause in infectious conjunctivitis: cohort study on informativeness of combinations of signs and symptoms. BMJ. 2004;329(7459): 206–210. doi: https://doi.org/10.1136/bmj.38128.631319.AE</mixed-citation></citation-alternatives></ref><ref id="cit131"><label>131</label><citation-alternatives><mixed-citation xml:lang="ru">Poorten VV, Hart A, Vauterin T, et al. Prognostic index for patients with parotid carcinoma: international external validation in a Belgian-German database. Cancer. 2009;115(3):540–550. doi: https://doi.org/10.1002/cncr.24015</mixed-citation><mixed-citation xml:lang="en">Poorten VV, Hart A, Vauterin T, et al. Prognostic index for patients with parotid carcinoma: international external validation in a Belgian-German database. Cancer. 2009;115(3):540–550. doi: https://doi.org/10.1002/cncr.24015</mixed-citation></citation-alternatives></ref><ref id="cit132"><label>132</label><citation-alternatives><mixed-citation xml:lang="ru">Moynihan R, Glassock R, Doust J. Chronic kidney disease controversy: how expanding definitions are unnecessarily labelling many people as diseased. BMJ. 2013;347:f4298. doi: https://doi.org/10.1136/bmj.f4298</mixed-citation><mixed-citation xml:lang="en">Moynihan R, Glassock R, Doust J. Chronic kidney disease controversy: how expanding definitions are unnecessarily labelling many people as diseased. BMJ. 2013;347:f4298. doi: https://doi.org/10.1136/bmj.f4298</mixed-citation></citation-alternatives></ref><ref id="cit133"><label>133</label><citation-alternatives><mixed-citation xml:lang="ru">Moynihan R, Henry D, Moons KG. Using evidence to combat overdiagnosis and overtreatment: evaluating treatments, tests, and disease definitions in the time of too much. PLoS Med. 2014;11(7):e1001655. doi: https://doi.org/10.1371/journal.pmed.1001655</mixed-citation><mixed-citation xml:lang="en">Moynihan R, Henry D, Moons KG. Using evidence to combat overdiagnosis and overtreatment: evaluating treatments, tests, and disease definitions in the time of too much. PLoS Med. 2014;11(7):e1001655. doi: https://doi.org/10.1371/journal.pmed.1001655</mixed-citation></citation-alternatives></ref><ref id="cit134"><label>134</label><citation-alternatives><mixed-citation xml:lang="ru">Dowling S, Spooner CH, Liang Y, et al. Accuracy of Ottawa Ankle Rules to exclude fractures of the ankle and midfoot in children: a meta-analysis. Acad Emerg Med. 2009;16(4):277–287. doi: https://doi.org/10.1111/j.1553-2712.2008.00333.x.</mixed-citation><mixed-citation xml:lang="en">Dowling S, Spooner CH, Liang Y, et al. Accuracy of Ottawa Ankle Rules to exclude fractures of the ankle and midfoot in children: a meta-analysis. Acad Emerg Med. 2009;16(4):277–287. doi: https://doi.org/10.1111/j.1553-2712.2008.00333.x.</mixed-citation></citation-alternatives></ref><ref id="cit135"><label>135</label><citation-alternatives><mixed-citation xml:lang="ru">Bachmann LM, Kolb E, Koller MT, et al. Accuracy of Ottawa ankle rules to exclude fractures of the ankle and mid-foot: systematic review. BMJ. 2003;326(7386):417. doi: https://doi.org/10.1136/bmj.326.7386.417.</mixed-citation><mixed-citation xml:lang="en">Bachmann LM, Kolb E, Koller MT, et al. Accuracy of Ottawa ankle rules to exclude fractures of the ankle and mid-foot: systematic review. BMJ. 2003;326(7386):417. doi: https://doi.org/10.1136/bmj.326.7386.417.</mixed-citation></citation-alternatives></ref><ref id="cit136"><label>136</label><citation-alternatives><mixed-citation xml:lang="ru">Büller HR, Ten Cate-Hoek AJ, Hoes AW, et al. Safely ruling out deep venous thrombosis in primary care. Ann Intern Med. 2009;150(4):229–235.</mixed-citation><mixed-citation xml:lang="en">Büller HR, Ten Cate-Hoek AJ, Hoes AW, et al. Safely ruling out deep venous thrombosis in primary care. Ann Intern Med. 2009;150(4):229–235.</mixed-citation></citation-alternatives></ref><ref id="cit137"><label>137</label><citation-alternatives><mixed-citation xml:lang="ru">Sparks AB, Struble CA, Wang ET, et al. Noninvasive prenatal detection and selective analysis of cell-free DNA obtained from maternal blood: evaluation for trisomy 21 and trisomy 18. Am J Obstet Gynecol. 2012;206(4):319.e1–9. doi: https://doi.org/10.1016/j.ajog.2012.01.030</mixed-citation><mixed-citation xml:lang="en">Sparks AB, Struble CA, Wang ET, et al. Noninvasive prenatal detection and selective analysis of cell-free DNA obtained from maternal blood: evaluation for trisomy 21 and trisomy 18. Am J Obstet Gynecol. 2012;206(4):319.e1–9. doi: https://doi.org/10.1016/j.ajog.2012.01.030</mixed-citation></citation-alternatives></ref><ref id="cit138"><label>138</label><citation-alternatives><mixed-citation xml:lang="ru">Ankerst DP, Boeck A, Freedland SJ, et al. Evaluating the PCPT risk calculator in ten international biopsy cohorts: results from the Prostate Biopsy Collaborative Group. World J Urol. 2012;30(2): 181–187. doi: https://doi.org/10.1007/s00345-011-0818-5</mixed-citation><mixed-citation xml:lang="en">Ankerst DP, Boeck A, Freedland SJ, et al. Evaluating the PCPT risk calculator in ten international biopsy cohorts: results from the Prostate Biopsy Collaborative Group. World J Urol. 2012;30(2): 181–187. doi: https://doi.org/10.1007/s00345-011-0818-5</mixed-citation></citation-alternatives></ref><ref id="cit139"><label>139</label><citation-alternatives><mixed-citation xml:lang="ru">Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ. 2008;336(7659):1475–1482. doi: https://doi.org/10.1136/bmj.39609.449676.25</mixed-citation><mixed-citation xml:lang="en">Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ. 2008;336(7659):1475–1482. doi: https://doi.org/10.1136/bmj.39609.449676.25</mixed-citation></citation-alternatives></ref><ref id="cit140"><label>140</label><citation-alternatives><mixed-citation xml:lang="ru">Conroy RM, Pyörälä K, Fitzgerald AP, et al. Estimation of tenyear risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003;24(11):987–1003. doi: https://doi.org/10.1016/s0195-668x(03)00114-3</mixed-citation><mixed-citation xml:lang="en">Conroy RM, Pyörälä K, Fitzgerald AP, et al. Estimation of tenyear risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003;24(11):987–1003. doi: https://doi.org/10.1016/s0195-668x(03)00114-3</mixed-citation></citation-alternatives></ref><ref id="cit141"><label>141</label><citation-alternatives><mixed-citation xml:lang="ru">Califf RM, Woodlief LH, Harrell FE, et al. Selection of thrombolytic therapy for individual patients: development of a clinical model. GUSTO-I Investigators. Am Heart J. 1997;133(6):630–639. doi: https://doi.org/10.1016/s0002-8703(97)70164-9</mixed-citation><mixed-citation xml:lang="en">Califf RM, Woodlief LH, Harrell FE, et al. Selection of thrombolytic therapy for individual patients: development of a clinical model. GUSTO-I Investigators. Am Heart J. 1997;133(6):630–639. doi: https://doi.org/10.1016/s0002-8703(97)70164-9</mixed-citation></citation-alternatives></ref><ref id="cit142"><label>142</label><citation-alternatives><mixed-citation xml:lang="ru">McCowan C, Donnan PT, Dewar J, et al. Identifying suspected breast cancer: development and validation of a clinical prediction rule. Br J Gen Pract. 2011;61:e205–e214. doi: https://doi.org/10.3399/bjgp11X572391</mixed-citation><mixed-citation xml:lang="en">McCowan C, Donnan PT, Dewar J, et al. Identifying suspected breast cancer: development and validation of a clinical prediction rule. Br J Gen Pract. 2011;61:e205–e214. doi: https://doi.org/10.3399/bjgp11X572391</mixed-citation></citation-alternatives></ref><ref id="cit143"><label>143</label><citation-alternatives><mixed-citation xml:lang="ru">Campbell HE, Gray AM, Harris AL, et al. Estimation and external validation of a new prognostic model for predicting recurrence-free survival for early breast cancer patients in the UK. Br J Cancer. 2010;103(6):776–786. doi: https://doi.org/10.1038/sj.bjc.6605863</mixed-citation><mixed-citation xml:lang="en">Campbell HE, Gray AM, Harris AL, et al. Estimation and external validation of a new prognostic model for predicting recurrence-free survival for early breast cancer patients in the UK. Br J Cancer. 2010;103(6):776–786. doi: https://doi.org/10.1038/sj.bjc.6605863</mixed-citation></citation-alternatives></ref><ref id="cit144"><label>144</label><citation-alternatives><mixed-citation xml:lang="ru">Wilson PW, D’Agostino RB, Levy D, et al. Prediction of coronary heart disease using risk factor categories. Circulation. 1998; 97(18):1837–1847. doi: https://doi.org/10.1161/01.cir.97.18.1837</mixed-citation><mixed-citation xml:lang="en">Wilson PW, D’Agostino RB, Levy D, et al. Prediction of coronary heart disease using risk factor categories. Circulation. 1998; 97(18):1837–1847. doi: https://doi.org/10.1161/01.cir.97.18.1837</mixed-citation></citation-alternatives></ref><ref id="cit145"><label>145</label><citation-alternatives><mixed-citation xml:lang="ru">Kengne AP, Patel A, Marre M, et al. Contemporary model for cardiovascular risk prediction in people with type 2 diabetes. Eur J Cardiovasc Prev Rehabil. 2011;18(3):393–398. doi: https://doi.org/10.1177/1741826710394270</mixed-citation><mixed-citation xml:lang="en">Kengne AP, Patel A, Marre M, et al. Contemporary model for cardiovascular risk prediction in people with type 2 diabetes. Eur J Cardiovasc Prev Rehabil. 2011;18(3):393–398. doi: https://doi.org/10.1177/1741826710394270</mixed-citation></citation-alternatives></ref><ref id="cit146"><label>146</label><citation-alternatives><mixed-citation xml:lang="ru">Appelboam A, Reuben AD, Benger JR, et al. Elbow extension test to rule out elbow fracture: multicentre, prospective validation and observational study of diagnostic accuracy in adults and children. BMJ. 2008;337:a2428. doi: https://doi.org/10.1136/bmj.a2428</mixed-citation><mixed-citation xml:lang="en">Appelboam A, Reuben AD, Benger JR, et al. Elbow extension test to rule out elbow fracture: multicentre, prospective validation and observational study of diagnostic accuracy in adults and children. BMJ. 2008;337:a2428. doi: https://doi.org/10.1136/bmj.a2428</mixed-citation></citation-alternatives></ref><ref id="cit147"><label>147</label><citation-alternatives><mixed-citation xml:lang="ru">Puhan MA, Hansel NN, Sobradillo P, et al; International COPD Cohorts Collaboration Working Group. Largescale international validation of the ADO index in subjects with COPD: an individual subject data analysis of 10 cohorts. BMJ Open. 2012;2(6):e002152. doi: https://doi.org/10.1136/bmjopen-2012-002152</mixed-citation><mixed-citation xml:lang="en">Puhan MA, Hansel NN, Sobradillo P, et al; International COPD Cohorts Collaboration Working Group. Largescale international validation of the ADO index in subjects with COPD: an individual subject data analysis of 10 cohorts. BMJ Open. 2012;2(6):e002152. doi: https://doi.org/10.1136/bmjopen-2012-002152</mixed-citation></citation-alternatives></ref><ref id="cit148"><label>148</label><citation-alternatives><mixed-citation xml:lang="ru">Knottnerus JA. The Evidence Base of Clinical Diagnosis. London: BMJ Books; 2002.</mixed-citation><mixed-citation xml:lang="en">Knottnerus JA. The Evidence Base of Clinical Diagnosis. London: BMJ Books; 2002.</mixed-citation></citation-alternatives></ref><ref id="cit149"><label>149</label><citation-alternatives><mixed-citation xml:lang="ru">Knottnerus JA, Muris JW. Assessment of the accuracy of diagnostic tests: the cross-sectional study. J Clin Epidemiol. 2003; 56(11):1118–1128. doi: https://doi.org/10.1016/s0895-4356(03)00206-3</mixed-citation><mixed-citation xml:lang="en">Knottnerus JA, Muris JW. Assessment of the accuracy of diagnostic tests: the cross-sectional study. J Clin Epidemiol. 2003; 56(11):1118–1128. doi: https://doi.org/10.1016/s0895-4356(03)00206-3</mixed-citation></citation-alternatives></ref><ref id="cit150"><label>150</label><citation-alternatives><mixed-citation xml:lang="ru">Grobbee DE, Hoes AW. Clinical Epidemiology: Principles, Methods, and Applications for Clinical Research. London: Jones and Bartlett Publishers; 2009.</mixed-citation><mixed-citation xml:lang="en">Grobbee DE, Hoes AW. Clinical Epidemiology: Principles, Methods, and Applications for Clinical Research. London: Jones and Bartlett Publishers; 2009.</mixed-citation></citation-alternatives></ref><ref id="cit151"><label>151</label><citation-alternatives><mixed-citation xml:lang="ru">Sackett DL, Tugwell P, Guyatt GH. Clinical Epidemiology: A Basic Science for Clinical Medicine. 2d ed. Boston: Little, Brown; 1991.</mixed-citation><mixed-citation xml:lang="en">Sackett DL, Tugwell P, Guyatt GH. Clinical Epidemiology: A Basic Science for Clinical Medicine. 2d ed. Boston: Little, Brown; 1991.</mixed-citation></citation-alternatives></ref><ref id="cit152"><label>152</label><citation-alternatives><mixed-citation xml:lang="ru">Biesheuvel CJ, Vergouwe Y, Oudega R, et al. Advantages of the nested case-control design in diagnostic research. BMC Med Res Methodol. 2008;8:48. doi: https://doi.org/10.1186/1471-2288-8-48</mixed-citation><mixed-citation xml:lang="en">Biesheuvel CJ, Vergouwe Y, Oudega R, et al. Advantages of the nested case-control design in diagnostic research. BMC Med Res Methodol. 2008;8:48. doi: https://doi.org/10.1186/1471-2288-8-48</mixed-citation></citation-alternatives></ref><ref id="cit153"><label>153</label><citation-alternatives><mixed-citation xml:lang="ru">Knottnerus JA, Dinant GJ. Medicine based evidence, a prerequisite for evidence based medicine. BMJ. 1997;315(7116): 1109–1110. doi: https://doi.org/10.1136/bmj.315.7116.1109</mixed-citation><mixed-citation xml:lang="en">Knottnerus JA, Dinant GJ. Medicine based evidence, a prerequisite for evidence based medicine. BMJ. 1997;315(7116): 1109–1110. doi: https://doi.org/10.1136/bmj.315.7116.1109</mixed-citation></citation-alternatives></ref><ref id="cit154"><label>154</label><citation-alternatives><mixed-citation xml:lang="ru">Knottnerus JA, vanWeel C, Muris JW. Evaluation of diagnostic procedures. BMJ. 2002;324(7335):477–480. doi: https://doi.org/10.1136/bmj.324.7335.477</mixed-citation><mixed-citation xml:lang="en">Knottnerus JA, vanWeel C, Muris JW. Evaluation of diagnostic procedures. BMJ. 2002;324(7335):477–480. doi: https://doi.org/10.1136/bmj.324.7335.477</mixed-citation></citation-alternatives></ref><ref id="cit155"><label>155</label><citation-alternatives><mixed-citation xml:lang="ru">Rutjes AW, Reitsma JB, Vandenbroucke JP, et al. Casecontrol and two-gate designs in diagnostic accuracy studies. Clin Chem. 2005;51(8):1335–1341. doi: https://doi.org/10.1373/clinchem.2005.048595</mixed-citation><mixed-citation xml:lang="en">Rutjes AW, Reitsma JB, Vandenbroucke JP, et al. Casecontrol and two-gate designs in diagnostic accuracy studies. Clin Chem. 2005;51(8):1335–1341. doi: https://doi.org/10.1373/clinchem.2005.048595</mixed-citation></citation-alternatives></ref><ref id="cit156"><label>156</label><citation-alternatives><mixed-citation xml:lang="ru">Lijmer JG, Mol BW, Heisterkamp S, et al. Empirical evidence of design-related bias in studies of diagnostic tests. JAMA. 1999;282(11):1061–1066. doi: https://doi.org/10.1001/jama.282.11.1061</mixed-citation><mixed-citation xml:lang="en">Lijmer JG, Mol BW, Heisterkamp S, et al. Empirical evidence of design-related bias in studies of diagnostic tests. JAMA. 1999;282(11):1061–1066. doi: https://doi.org/10.1001/jama.282.11.1061</mixed-citation></citation-alternatives></ref><ref id="cit157"><label>157</label><citation-alternatives><mixed-citation xml:lang="ru">van Zaane B, Vergouwe Y, Donders AR, Moons KG. Comparison of approaches to estimate confidence intervals of post-test probabilities of diagnostic test results in a nested case-control study. BMC Med Res Methodol. 2012;12:166. doi: https://doi.org/10.1186/1471-2288-12-166</mixed-citation><mixed-citation xml:lang="en">van Zaane B, Vergouwe Y, Donders AR, Moons KG. Comparison of approaches to estimate confidence intervals of post-test probabilities of diagnostic test results in a nested case-control study. BMC Med Res Methodol. 2012;12:166. doi: https://doi.org/10.1186/1471-2288-12-166</mixed-citation></citation-alternatives></ref><ref id="cit158"><label>158</label><citation-alternatives><mixed-citation xml:lang="ru">Lumbreras B, Parker LA, Porta M, et al. Overinterpretation of clinical applicability in molecular diagnostic research. Clin Chem. 2009;55(4):786–794. doi: https://doi.org/10.1373/clinchem.2008.121517</mixed-citation><mixed-citation xml:lang="en">Lumbreras B, Parker LA, Porta M, et al. Overinterpretation of clinical applicability in molecular diagnostic research. Clin Chem. 2009;55(4):786–794. doi: https://doi.org/10.1373/clinchem.2008.121517</mixed-citation></citation-alternatives></ref><ref id="cit159"><label>159</label><citation-alternatives><mixed-citation xml:lang="ru">Tzoulaki I, Siontis KC, Ioannidis JP. Prognostic effect size of cardiovascular biomarkers in datasets from observational studies versus randomised trials: meta-epidemiology study. BMJ. 2011;343:d6829. doi: https://doi.org/10.1136/bmj.d6829</mixed-citation><mixed-citation xml:lang="en">Tzoulaki I, Siontis KC, Ioannidis JP. Prognostic effect size of cardiovascular biomarkers in datasets from observational studies versus randomised trials: meta-epidemiology study. BMJ. 2011;343:d6829. doi: https://doi.org/10.1136/bmj.d6829</mixed-citation></citation-alternatives></ref><ref id="cit160"><label>160</label><citation-alternatives><mixed-citation xml:lang="ru">Greving JP, Wermer MJ, Brown RD, et al. Development of the PHASES score for prediction of risk of rupture of intracranial aneurysms: a pooled analysis of six prospective cohort studies. Lancet Neurol. 2014;13(1):59–66. doi: https://doi.org/10.1016/S1474-4422(13)70263-1</mixed-citation><mixed-citation xml:lang="en">Greving JP, Wermer MJ, Brown RD, et al. Development of the PHASES score for prediction of risk of rupture of intracranial aneurysms: a pooled analysis of six prospective cohort studies. Lancet Neurol. 2014;13(1):59–66. doi: https://doi.org/10.1016/S1474-4422(13)70263-1</mixed-citation></citation-alternatives></ref><ref id="cit161"><label>161</label><citation-alternatives><mixed-citation xml:lang="ru">Collins GS, Altman DG. Predicting the adverse risk of statin treatment: an independent and external validation of Qstatin risk scores in the UK. Heart. 2012;98(14):1091–1097. doi: https://doi.org/10.1136/heartjnl-2012-302014</mixed-citation><mixed-citation xml:lang="en">Collins GS, Altman DG. Predicting the adverse risk of statin treatment: an independent and external validation of Qstatin risk scores in the UK. Heart. 2012;98(14):1091–1097. doi: https://doi.org/10.1136/heartjnl-2012-302014</mixed-citation></citation-alternatives></ref><ref id="cit162"><label>162</label><citation-alternatives><mixed-citation xml:lang="ru">Glickman SW, Shofer FS, Wu MC, et al. Development and validation of a prioritization rule for obtaining an immediate 12-lead electrocardiogram in the emergency department to identify ST-elevation myocardial infarction. Am Heart J. 2012;163(3): 372–382. doi: https://doi.org/10.1016/j.ahj.2011.10.021</mixed-citation><mixed-citation xml:lang="en">Glickman SW, Shofer FS, Wu MC, et al. Development and validation of a prioritization rule for obtaining an immediate 12-lead electrocardiogram in the emergency department to identify ST-elevation myocardial infarction. Am Heart J. 2012;163(3): 372–382. doi: https://doi.org/10.1016/j.ahj.2011.10.021</mixed-citation></citation-alternatives></ref><ref id="cit163"><label>163</label><citation-alternatives><mixed-citation xml:lang="ru">Debray TP, Koffijberg H, Lu D, et al. Incorporating published univariable associations in diagnostic and prognostic modeling. BMC Med Res Methodol. 2012;12:121. doi: https://doi.org/10.1186/1471-2288-12-121</mixed-citation><mixed-citation xml:lang="en">Debray TP, Koffijberg H, Lu D, et al. Incorporating published univariable associations in diagnostic and prognostic modeling. BMC Med Res Methodol. 2012;12:121. doi: https://doi.org/10.1186/1471-2288-12-121</mixed-citation></citation-alternatives></ref><ref id="cit164"><label>164</label><citation-alternatives><mixed-citation xml:lang="ru">Debray TP, Koffijberg H, Vergouwe Y, et al. Aggregating published prediction models with individual participant data: a comparison of different approaches. Stat Med. 2012;31(23):2697-712. doi: https://doi.org/10.1002/sim.5412</mixed-citation><mixed-citation xml:lang="en">Debray TP, Koffijberg H, Vergouwe Y, et al. Aggregating published prediction models with individual participant data: a comparison of different approaches. Stat Med. 2012;31(23):2697-712. doi: https://doi.org/10.1002/sim.5412</mixed-citation></citation-alternatives></ref><ref id="cit165"><label>165</label><citation-alternatives><mixed-citation xml:lang="ru">Debray TP, Moons KG, Abo-Zaid GM, et al. Individual participant data meta-analysis for a binary outcome: onestage or two-stage? PLoS One. 2013;8(4):e60650. doi: https://doi.org/10.1371/journal.pone.0060650</mixed-citation><mixed-citation xml:lang="en">Debray TP, Moons KG, Abo-Zaid GM, et al. Individual participant data meta-analysis for a binary outcome: onestage or two-stage? PLoS One. 2013;8(4):e60650. doi: https://doi.org/10.1371/journal.pone.0060650</mixed-citation></citation-alternatives></ref><ref id="cit166"><label>166</label><citation-alternatives><mixed-citation xml:lang="ru">Debray TP, Moons KG, Ahmed I, et al. A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis. Stat Med. 2013;32(18):3158–3180. doi: https://doi.org/10.1002/sim.5732</mixed-citation><mixed-citation xml:lang="en">Debray TP, Moons KG, Ahmed I, et al. A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis. Stat Med. 2013;32(18):3158–3180. doi: https://doi.org/10.1002/sim.5732</mixed-citation></citation-alternatives></ref><ref id="cit167"><label>167</label><citation-alternatives><mixed-citation xml:lang="ru">Bouwmeester W, Twisk JW, Kappen TH, et al. Prediction models for clustered data: comparison of a random intercept and standard regression model. BMC Med Res Methodol. 2013;13:19. doi: https://doi.org/10.1186/1471-2288-13-19</mixed-citation><mixed-citation xml:lang="en">Bouwmeester W, Twisk JW, Kappen TH, et al. Prediction models for clustered data: comparison of a random intercept and standard regression model. BMC Med Res Methodol. 2013;13:19. doi: https://doi.org/10.1186/1471-2288-13-19</mixed-citation></citation-alternatives></ref><ref id="cit168"><label>168</label><citation-alternatives><mixed-citation xml:lang="ru">Bouwmeester W, Moons KG, Happen TH, et al. Internal validation of risk models in clustered data: a comparison of bootstrap schemes. Am J Epidemiol. 2013;177(11):1209–1217. doi: https://doi.org/10.1093/aje/kws396</mixed-citation><mixed-citation xml:lang="en">Bouwmeester W, Moons KG, Happen TH, et al. Internal validation of risk models in clustered data: a comparison of bootstrap schemes. Am J Epidemiol. 2013;177(11):1209–1217. doi: https://doi.org/10.1093/aje/kws396</mixed-citation></citation-alternatives></ref><ref id="cit169"><label>169</label><citation-alternatives><mixed-citation xml:lang="ru">Rosner B, Qiu W, Lee ML. Assessing discrimination of risk prediction rules in a clustered data setting. Lifetime Data Anal. 2013; 19(2):242–256. doi: https://doi.org/10.1007/s10985-012-9240-6</mixed-citation><mixed-citation xml:lang="en">Rosner B, Qiu W, Lee ML. Assessing discrimination of risk prediction rules in a clustered data setting. Lifetime Data Anal. 2013; 19(2):242–256. doi: https://doi.org/10.1007/s10985-012-9240-6</mixed-citation></citation-alternatives></ref><ref id="cit170"><label>170</label><citation-alternatives><mixed-citation xml:lang="ru">van Klaveren D, Steyerberg EW, Perel P, Vergouwe Y. Assessing discriminative ability of risk models in clustered data. BMC Med Res Methodol. 2014;14:5. doi: https://doi.org/10.1186/1471-2288-14-5</mixed-citation><mixed-citation xml:lang="en">van Klaveren D, Steyerberg EW, Perel P, Vergouwe Y. Assessing discriminative ability of risk models in clustered data. BMC Med Res Methodol. 2014;14:5. doi: https://doi.org/10.1186/1471-2288-14-5</mixed-citation></citation-alternatives></ref><ref id="cit171"><label>171</label><citation-alternatives><mixed-citation xml:lang="ru">van Klaveren D, Steyerberg EW, Vergouwe Y. Interpretation of concordance measures for clustered data. Stat Med. 2014;33(4):714–716. doi: https://doi.org/10.1002/sim.5928</mixed-citation><mixed-citation xml:lang="en">van Klaveren D, Steyerberg EW, Vergouwe Y. Interpretation of concordance measures for clustered data. Stat Med. 2014;33(4):714–716. doi: https://doi.org/10.1002/sim.5928</mixed-citation></citation-alternatives></ref><ref id="cit172"><label>172</label><citation-alternatives><mixed-citation xml:lang="ru">Sanderson J, Thompson SG, White IR, et al. Derivation and assessment of risk prediction models using case-cohort data. BMC Med Res Methodol. 2013;13:113. doi: https://doi.org/10.1186/1471-2288-13-113</mixed-citation><mixed-citation xml:lang="en">Sanderson J, Thompson SG, White IR, et al. Derivation and assessment of risk prediction models using case-cohort data. BMC Med Res Methodol. 2013;13:113. doi: https://doi.org/10.1186/1471-2288-13-113</mixed-citation></citation-alternatives></ref><ref id="cit173"><label>173</label><citation-alternatives><mixed-citation xml:lang="ru">Ganna A, Reilly M, de Faire U, et al. Risk prediction measures for case-cohort and nested case-control designs: an application to cardiovascular disease. Am J Epidemiol. 2012;175(7):715–724. doi: https://doi.org/10.1093/aje/kwr374</mixed-citation><mixed-citation xml:lang="en">Ganna A, Reilly M, de Faire U, et al. Risk prediction measures for case-cohort and nested case-control designs: an application to cardiovascular disease. Am J Epidemiol. 2012;175(7):715–724. doi: https://doi.org/10.1093/aje/kwr374</mixed-citation></citation-alternatives></ref><ref id="cit174"><label>174</label><citation-alternatives><mixed-citation xml:lang="ru">Kulathinal S, Karvanen J, Saarela O, Kuulasmaa K. Casecohort design in practice — experiences from the MORGAM Project. Epidemiol Perspect Innov. 2007;4:15. doi: https://doi.org/10.1186/1742-5573-4-15</mixed-citation><mixed-citation xml:lang="en">Kulathinal S, Karvanen J, Saarela O, Kuulasmaa K. Casecohort design in practice — experiences from the MORGAM Project. Epidemiol Perspect Innov. 2007;4:15. doi: https://doi.org/10.1186/1742-5573-4-15</mixed-citation></citation-alternatives></ref><ref id="cit175"><label>175</label><citation-alternatives><mixed-citation xml:lang="ru">Kengne AP, Beulens JW, Peelen LM, et al. Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. Lancet Diabetes Endocrinol. 2014;2(1):19–29. doi: https://doi.org/10.1016/S2213-8587(13)70103-7</mixed-citation><mixed-citation xml:lang="en">Kengne AP, Beulens JW, Peelen LM, et al. Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. Lancet Diabetes Endocrinol. 2014;2(1):19–29. doi: https://doi.org/10.1016/S2213-8587(13)70103-7</mixed-citation></citation-alternatives></ref><ref id="cit176"><label>176</label><citation-alternatives><mixed-citation xml:lang="ru">Alba AC, Agoritsas T, Jankowski M, et al. Risk prediction models for mortality in ambulatory heart failure patients: a systematic review. Circ Heart Fail. 2013;6(5):881–889. doi: https://doi.org/10.1161/CIRCHEARTFAILURE.112.000043</mixed-citation><mixed-citation xml:lang="en">Alba AC, Agoritsas T, Jankowski M, et al. Risk prediction models for mortality in ambulatory heart failure patients: a systematic review. Circ Heart Fail. 2013;6(5):881–889. doi: https://doi.org/10.1161/CIRCHEARTFAILURE.112.000043</mixed-citation></citation-alternatives></ref><ref id="cit177"><label>177</label><citation-alternatives><mixed-citation xml:lang="ru">Arkenau HT, Barriuso J, Olmos D, et al. Prospective validation of a prognostic score to improve patient selection for oncology phase I trials. J Clin Oncol. 2009;27(16):2692–2696. doi: https://doi.org/10.1200/JCO.2008.19.5081</mixed-citation><mixed-citation xml:lang="en">Arkenau HT, Barriuso J, Olmos D, et al. Prospective validation of a prognostic score to improve patient selection for oncology phase I trials. J Clin Oncol. 2009;27(16):2692–2696. doi: https://doi.org/10.1200/JCO.2008.19.5081</mixed-citation></citation-alternatives></ref><ref id="cit178"><label>178</label><citation-alternatives><mixed-citation xml:lang="ru">Ronga A, Vaucher P, Haasenritter J, et al. Development and validation of a clinical prediction rule for chest wall syndrome in primary care. BMC Fam Pract. 2012;13:74. doi: https://doi.org/10.1186/1471-2296-13-74</mixed-citation><mixed-citation xml:lang="en">Ronga A, Vaucher P, Haasenritter J, et al. Development and validation of a clinical prediction rule for chest wall syndrome in primary care. BMC Fam Pract. 2012;13:74. doi: https://doi.org/10.1186/1471-2296-13-74</mixed-citation></citation-alternatives></ref><ref id="cit179"><label>179</label><citation-alternatives><mixed-citation xml:lang="ru">Martinez JA, Belastegui A, Basabe I, et al. Derivation and validation of a clinical prediction rule for delirium in patients admitted to a medical ward: an observational study. BMJ Open. 2012;2(5):e001599. doi: https://doi.org/10.1136/bmjopen-2012-001599</mixed-citation><mixed-citation xml:lang="en">Martinez JA, Belastegui A, Basabe I, et al. Derivation and validation of a clinical prediction rule for delirium in patients admitted to a medical ward: an observational study. BMJ Open. 2012;2(5):e001599. doi: https://doi.org/10.1136/bmjopen-2012-001599</mixed-citation></citation-alternatives></ref><ref id="cit180"><label>180</label><citation-alternatives><mixed-citation xml:lang="ru">Rahimi K, Bennett D, Conrad N, et al. Risk prediction in patients with heart failure: a systematic review and analysis. JACC Heart Fail. 2014;2(5):440–446. doi: https://doi.org/10.1016/j.jchf.2014.04.008</mixed-citation><mixed-citation xml:lang="en">Rahimi K, Bennett D, Conrad N, et al. Risk prediction in patients with heart failure: a systematic review and analysis. JACC Heart Fail. 2014;2(5):440–446. doi: https://doi.org/10.1016/j.jchf.2014.04.008</mixed-citation></citation-alternatives></ref><ref id="cit181"><label>181</label><citation-alternatives><mixed-citation xml:lang="ru">Ebell MH, Afonson AM, Gonzales R, et al. Development and validation of a clinical decision rule for the diagnosis of influenza. J Am Board Fam Med. 2012;25(1):55–62. doi: https://doi.org/10.3122/jabfm.2012.01.110161</mixed-citation><mixed-citation xml:lang="en">Ebell MH, Afonson AM, Gonzales R, et al. Development and validation of a clinical decision rule for the diagnosis of influenza. J Am Board Fam Med. 2012;25(1):55–62. doi: https://doi.org/10.3122/jabfm.2012.01.110161</mixed-citation></citation-alternatives></ref><ref id="cit182"><label>182</label><citation-alternatives><mixed-citation xml:lang="ru">Counsell C, Dennis M. Systematic review of prognostic models in patients with acute stroke. Cerebrovasc Dis. 2001;12(3): 159–170. doi: https://doi.org/10.1159/000047699</mixed-citation><mixed-citation xml:lang="en">Counsell C, Dennis M. Systematic review of prognostic models in patients with acute stroke. Cerebrovasc Dis. 2001;12(3): 159–170. doi: https://doi.org/10.1159/000047699</mixed-citation></citation-alternatives></ref><ref id="cit183"><label>183</label><citation-alternatives><mixed-citation xml:lang="ru">Knottnerus JA. Between iatrotropic stimulus and interiatric referral: the domain of primary care research. J Clin Epidemiol. 2002;55(12):1201–1206. doi: https://doi.org/10.1016/s0895-4356(02)00528-0</mixed-citation><mixed-citation xml:lang="en">Knottnerus JA. Between iatrotropic stimulus and interiatric referral: the domain of primary care research. J Clin Epidemiol. 2002;55(12):1201–1206. doi: https://doi.org/10.1016/s0895-4356(02)00528-0</mixed-citation></citation-alternatives></ref><ref id="cit184"><label>184</label><citation-alternatives><mixed-citation xml:lang="ru">Moreno R, Apolone G. Impact of different customization strategies in the performance of a general severity score. Crit Care Med. 1997;25(12):2001–2008. doi: https://doi.org/10.1097/00003246-199712000-00017</mixed-citation><mixed-citation xml:lang="en">Moreno R, Apolone G. Impact of different customization strategies in the performance of a general severity score. Crit Care Med. 1997;25(12):2001–2008. doi: https://doi.org/10.1097/00003246-199712000-00017</mixed-citation></citation-alternatives></ref><ref id="cit185"><label>185</label><citation-alternatives><mixed-citation xml:lang="ru">Tu JV, Austin PC, Walld R, et al. Development and validation of the Ontario acute myocardial infarction mortality prediction rules. J Am Coll Cardiol. 2001;37(4):992–997. doi: https://doi.org/10.1016/s0735-1097(01)01109-3</mixed-citation><mixed-citation xml:lang="en">Tu JV, Austin PC, Walld R, et al. Development and validation of the Ontario acute myocardial infarction mortality prediction rules. J Am Coll Cardiol. 2001;37(4):992–997. doi: https://doi.org/10.1016/s0735-1097(01)01109-3</mixed-citation></citation-alternatives></ref><ref id="cit186"><label>186</label><citation-alternatives><mixed-citation xml:lang="ru">Vergouwe Y, Moons KG, Steyerberg EW. External validity of risk models: use of benchmark values to disentangle a case-mix effect from incorrect coefficients. Am J Epidemiol. 2010;172(8):971–980. doi: https://doi.org/10.1093/aje/kwq223</mixed-citation><mixed-citation xml:lang="en">Vergouwe Y, Moons KG, Steyerberg EW. External validity of risk models: use of benchmark values to disentangle a case-mix effect from incorrect coefficients. Am J Epidemiol. 2010;172(8):971–980. doi: https://doi.org/10.1093/aje/kwq223</mixed-citation></citation-alternatives></ref><ref id="cit187"><label>187</label><citation-alternatives><mixed-citation xml:lang="ru">Kappen TH, Vergouwe Y, van Klei WA, et al. Adaptation of clinical prediction models for application in local settings. Med Decis Making. 2012;32(3):E1–E10. doi: https://doi.org/10.1177/0272989X12439755</mixed-citation><mixed-citation xml:lang="en">Kappen TH, Vergouwe Y, van Klei WA, et al. Adaptation of clinical prediction models for application in local settings. Med Decis Making. 2012;32(3):E1–E10. doi: https://doi.org/10.1177/0272989X12439755</mixed-citation></citation-alternatives></ref><ref id="cit188"><label>188</label><citation-alternatives><mixed-citation xml:lang="ru">Oudega R, Hoes AW, Moons KG. The Wells rule does not adequately rule out deep venous thrombosis in primary care patients. Ann Intern Med. 2005;143(2):100–107. doi: https://doi.org/10.7326/0003-4819-143-2-200507190-00008</mixed-citation><mixed-citation xml:lang="en">Oudega R, Hoes AW, Moons KG. The Wells rule does not adequately rule out deep venous thrombosis in primary care patients. Ann Intern Med. 2005;143(2):100–107. doi: https://doi.org/10.7326/0003-4819-143-2-200507190-00008</mixed-citation></citation-alternatives></ref><ref id="cit189"><label>189</label><citation-alternatives><mixed-citation xml:lang="ru">Knottnerus JA, Leffers P. The influence of referral patterns on the characteristics of diagnostic tests. J Clin Epidemiol. 1992;45(10):1143–1154. doi: https://doi.org/10.1016/0895-4356(92)90155-g</mixed-citation><mixed-citation xml:lang="en">Knottnerus JA, Leffers P. The influence of referral patterns on the characteristics of diagnostic tests. J Clin Epidemiol. 1992;45(10):1143–1154. doi: https://doi.org/10.1016/0895-4356(92)90155-g</mixed-citation></citation-alternatives></ref><ref id="cit190"><label>190</label><citation-alternatives><mixed-citation xml:lang="ru">Knottnerus JA. The effects of disease verification and referral on the relationship between symptoms and diseases. Med Decis Making. 1987;7(3):139–148. doi: https://doi.org/10.1177/0272989X8700700304</mixed-citation><mixed-citation xml:lang="en">Knottnerus JA. The effects of disease verification and referral on the relationship between symptoms and diseases. Med Decis Making. 1987;7(3):139–148. doi: https://doi.org/10.1177/0272989X8700700304</mixed-citation></citation-alternatives></ref><ref id="cit191"><label>191</label><citation-alternatives><mixed-citation xml:lang="ru">Eberhart LH, Morin AM, Guber D, et al. Applicability of risk scores for postoperative nausea and vomiting in adults to paediatric patients. Br J Anaesth. 2004;93(3):386–392. doi: https://doi.org/10.1093/bja/aeh221</mixed-citation><mixed-citation xml:lang="en">Eberhart LH, Morin AM, Guber D, et al. Applicability of risk scores for postoperative nausea and vomiting in adults to paediatric patients. Br J Anaesth. 2004;93(3):386–392. doi: https://doi.org/10.1093/bja/aeh221</mixed-citation></citation-alternatives></ref><ref id="cit192"><label>192</label><citation-alternatives><mixed-citation xml:lang="ru">Debray TP, Vergouwe Y, Koffijberg H, et al. A new framework to enhance the interpretation of external validation studies of clinical prediction models. J Clin Epidemiol. 2015;68(3):279–289. doi: https://doi.org/10.1016/j.jclinepi.2014.06.018</mixed-citation><mixed-citation xml:lang="en">Debray TP, Vergouwe Y, Koffijberg H, et al. A new framework to enhance the interpretation of external validation studies of clinical prediction models. J Clin Epidemiol. 2015;68(3):279–289. doi: https://doi.org/10.1016/j.jclinepi.2014.06.018</mixed-citation></citation-alternatives></ref><ref id="cit193"><label>193</label><citation-alternatives><mixed-citation xml:lang="ru">Klemke CD, Mansmann U, Poenitz N, et al. Prognostic factors and prediction of prognosis by the CTCL Severity Index in mycosis fungoides and Sézary syndrome. Br J Dermatol. 2005;153(1): 118–124. doi: https://doi.org/10.1111/j.1365-2133.2005.06676.x</mixed-citation><mixed-citation xml:lang="en">Klemke CD, Mansmann U, Poenitz N, et al. Prognostic factors and prediction of prognosis by the CTCL Severity Index in mycosis fungoides and Sézary syndrome. Br J Dermatol. 2005;153(1): 118–124. doi: https://doi.org/10.1111/j.1365-2133.2005.06676.x</mixed-citation></citation-alternatives></ref><ref id="cit194"><label>194</label><citation-alternatives><mixed-citation xml:lang="ru">Tay SY, Thoo FL, Sitoh YY, et al. The Ottawa Ankle Rules in Asia: validating a clinical decision rule for requesting X-rays in twisting ankle and foot injuries. J Emerg Med. 1999;17(6):945–947. doi: https://doi.org/10.1016/s0736-4679(99)00120-1</mixed-citation><mixed-citation xml:lang="en">Tay SY, Thoo FL, Sitoh YY, et al. The Ottawa Ankle Rules in Asia: validating a clinical decision rule for requesting X-rays in twisting ankle and foot injuries. J Emerg Med. 1999;17(6):945–947. doi: https://doi.org/10.1016/s0736-4679(99)00120-1</mixed-citation></citation-alternatives></ref><ref id="cit195"><label>195</label><citation-alternatives><mixed-citation xml:lang="ru">Donders AR, van der Heijden GJ, Stijnen T, Moons KG. Review: a gentle introduction to imputation of missing values. J Clin Epidemiol. 2006;59(10):1087–1091. doi: https://doi.org/10.1016/j.jclinepi.2006.01.014</mixed-citation><mixed-citation xml:lang="en">Donders AR, van der Heijden GJ, Stijnen T, Moons KG. Review: a gentle introduction to imputation of missing values. J Clin Epidemiol. 2006;59(10):1087–1091. doi: https://doi.org/10.1016/j.jclinepi.2006.01.014</mixed-citation></citation-alternatives></ref><ref id="cit196"><label>196</label><citation-alternatives><mixed-citation xml:lang="ru">Groenwold RH, White IR, Donders AR, et al. Missing covariate data in clinical research: when and when not to use the missingindicator method for analysis. CMAJ. 2012;184(11):1265–1269. doi: https://doi.org/10.1503/cmaj.110977</mixed-citation><mixed-citation xml:lang="en">Groenwold RH, White IR, Donders AR, et al. Missing covariate data in clinical research: when and when not to use the missingindicator method for analysis. CMAJ. 2012;184(11):1265–1269. doi: https://doi.org/10.1503/cmaj.110977</mixed-citation></citation-alternatives></ref><ref id="cit197"><label>197</label><citation-alternatives><mixed-citation xml:lang="ru">Janssen KJ, Donders AR, Harrell FE, et al. Missing covariate data in medical research: to impute is better than to ignore. J Clin Epidemiol. 2010;63(7):721–727. doi: https://doi.org/10.1016/j.jclinepi.2009.12.008</mixed-citation><mixed-citation xml:lang="en">Janssen KJ, Donders AR, Harrell FE, et al. Missing covariate data in medical research: to impute is better than to ignore. J Clin Epidemiol. 2010;63(7):721–727. doi: https://doi.org/10.1016/j.jclinepi.2009.12.008</mixed-citation></citation-alternatives></ref><ref id="cit198"><label>198</label><citation-alternatives><mixed-citation xml:lang="ru">Janssen KJ, Vergouwe Y, Donders AR, et al. Dealing with missing predictor values when applying clinical prediction models. Clin Chem. 2009;55(5):994–1001. doi: https://doi.org/10.1373/clinchem.2008.115345</mixed-citation><mixed-citation xml:lang="en">Janssen KJ, Vergouwe Y, Donders AR, et al. Dealing with missing predictor values when applying clinical prediction models. Clin Chem. 2009;55(5):994–1001. doi: https://doi.org/10.1373/clinchem.2008.115345</mixed-citation></citation-alternatives></ref><ref id="cit199"><label>199</label><citation-alternatives><mixed-citation xml:lang="ru">Moons KG, Donders RA, Stijnen T, Harrell FE. Using the outcome for imputation of missing predictor values was preferred. J Clin Epidemiol. 2006;59(10):1092–1101. doi: https://doi.org/10.1016/j.jclinepi.2006.01.009</mixed-citation><mixed-citation xml:lang="en">Moons KG, Donders RA, Stijnen T, Harrell FE. Using the outcome for imputation of missing predictor values was preferred. J Clin Epidemiol. 2006;59(10):1092–1101. doi: https://doi.org/10.1016/j.jclinepi.2006.01.009</mixed-citation></citation-alternatives></ref><ref id="cit200"><label>200</label><citation-alternatives><mixed-citation xml:lang="ru">Sterne JA, White IR, Carlin JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393. doi: https://doi.org/10.1136/bmj.b2393</mixed-citation><mixed-citation xml:lang="en">Sterne JA, White IR, Carlin JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393. doi: https://doi.org/10.1136/bmj.b2393</mixed-citation></citation-alternatives></ref><ref id="cit201"><label>201</label><citation-alternatives><mixed-citation xml:lang="ru">Vergouwe Y, Royston P, Moons KG, Altman DG. Development and validation of a prediction model with missing predictor data: a practical approach. J Clin Epidemiol. 2010;63(2):205–214. doi: https://doi.org/10.1016/j.jclinepi.2009.03.017</mixed-citation><mixed-citation xml:lang="en">Vergouwe Y, Royston P, Moons KG, Altman DG. Development and validation of a prediction model with missing predictor data: a practical approach. J Clin Epidemiol. 2010;63(2):205–214. doi: https://doi.org/10.1016/j.jclinepi.2009.03.017</mixed-citation></citation-alternatives></ref><ref id="cit202"><label>202</label><citation-alternatives><mixed-citation xml:lang="ru">Hemingway H, Croft P, Perel P, et al. Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes. BMJ. 2013;346:35595. doi: https://doi.org/10.1136/bmj.e5595</mixed-citation><mixed-citation xml:lang="en">Hemingway H, Croft P, Perel P, et al. Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes. BMJ. 2013;346:35595. doi: https://doi.org/10.1136/bmj.e5595</mixed-citation></citation-alternatives></ref><ref id="cit203"><label>203</label><citation-alternatives><mixed-citation xml:lang="ru">Liew SM, Doust J, Glasziou P. Cardiovascular risk scores do not account for the effect of treatment: a review. Heart. 2011;97(9): 689–697. doi: https://doi.org/10.1136/hrt.2010.220442</mixed-citation><mixed-citation xml:lang="en">Liew SM, Doust J, Glasziou P. Cardiovascular risk scores do not account for the effect of treatment: a review. Heart. 2011;97(9): 689–697. doi: https://doi.org/10.1136/hrt.2010.220442</mixed-citation></citation-alternatives></ref><ref id="cit204"><label>204</label><citation-alternatives><mixed-citation xml:lang="ru">Simon R, Altman DG. Statistical aspects of prognostic factor studies in oncology. Br J Cancer. 1994;69(6):979–985. doi: https://doi.org/10.1038/bjc.1994.192</mixed-citation><mixed-citation xml:lang="en">Simon R, Altman DG. Statistical aspects of prognostic factor studies in oncology. Br J Cancer. 1994;69(6):979–985. doi: https://doi.org/10.1038/bjc.1994.192</mixed-citation></citation-alternatives></ref><ref id="cit205"><label>205</label><citation-alternatives><mixed-citation xml:lang="ru">Landefeld CS, Goldman L. Major bleeding in outpatients treated with warfarin: incidence and prediction by factors known at the start of outpatient therapy. Am J Med. 1989;87(2):144–152. doi: https://doi.org/10.1016/s0002-9343(89)80689-8</mixed-citation><mixed-citation xml:lang="en">Landefeld CS, Goldman L. Major bleeding in outpatients treated with warfarin: incidence and prediction by factors known at the start of outpatient therapy. Am J Med. 1989;87(2):144–152. doi: https://doi.org/10.1016/s0002-9343(89)80689-8</mixed-citation></citation-alternatives></ref><ref id="cit206"><label>206</label><citation-alternatives><mixed-citation xml:lang="ru">Schuit E, Groenwold RH, Harrell FE, et al. Unexpected predictoroutcome associations in clinical prediction research: causes and solutions. CMAJ. 2013;185(10):E499–E505. doi: https://doi.org/10.1503/cmaj.120812</mixed-citation><mixed-citation xml:lang="en">Schuit E, Groenwold RH, Harrell FE, et al. Unexpected predictoroutcome associations in clinical prediction research: causes and solutions. CMAJ. 2013;185(10):E499–E505. doi: https://doi.org/10.1503/cmaj.120812</mixed-citation></citation-alternatives></ref><ref id="cit207"><label>207</label><citation-alternatives><mixed-citation xml:lang="ru">Wong J, Taljaard M, Forster AJ, et al. Addition of timedependent covariates to a survival model significantly improved predictions for daily risk of hospital death. J Eval Clin Pract. 2013;19(2):351–357. doi: https://doi.org/10.1111/j.1365-2753.2012.01832.x</mixed-citation><mixed-citation xml:lang="en">Wong J, Taljaard M, Forster AJ, et al. Addition of timedependent covariates to a survival model significantly improved predictions for daily risk of hospital death. J Eval Clin Pract. 2013;19(2):351–357. doi: https://doi.org/10.1111/j.1365-2753.2012.01832.x</mixed-citation></citation-alternatives></ref><ref id="cit208"><label>208</label><citation-alternatives><mixed-citation xml:lang="ru">Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA. 2007;297(6):611–619. doi: https://doi.org/10.1001/jama.297.6.611</mixed-citation><mixed-citation xml:lang="en">Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA. 2007;297(6):611–619. doi: https://doi.org/10.1001/jama.297.6.611</mixed-citation></citation-alternatives></ref><ref id="cit209"><label>209</label><citation-alternatives><mixed-citation xml:lang="ru">Reitsma JB, Rutjes AW, Khan KS, et al. A review of solutions for diagnostic accuracy studies with an imperfect or missing reference standard. J Clin Epidemiol. 2009;62(8):797–806. doi: https://doi.org/10.1016/j.jclinepi.2009.02.005</mixed-citation><mixed-citation xml:lang="en">Reitsma JB, Rutjes AW, Khan KS, et al. A review of solutions for diagnostic accuracy studies with an imperfect or missing reference standard. J Clin Epidemiol. 2009;62(8):797–806. doi: https://doi.org/10.1016/j.jclinepi.2009.02.005</mixed-citation></citation-alternatives></ref><ref id="cit210"><label>210</label><citation-alternatives><mixed-citation xml:lang="ru">Massing MW, Simpson RJ, Rautaharju PM, et al. Usefulness of ventricular premature complexes to predict coronary heart disease events and mortality (from the Atherosclerosis Risk In Communities cohort). Am J Cardiol. 2006;98(12):1609–1612. doi: https://doi.org/10.1016/j.amjcard.2006.06.061</mixed-citation><mixed-citation xml:lang="en">Massing MW, Simpson RJ, Rautaharju PM, et al. Usefulness of ventricular premature complexes to predict coronary heart disease events and mortality (from the Atherosclerosis Risk In Communities cohort). Am J Cardiol. 2006;98(12):1609–1612. doi: https://doi.org/10.1016/j.amjcard.2006.06.061</mixed-citation></citation-alternatives></ref><ref id="cit211"><label>211</label><citation-alternatives><mixed-citation xml:lang="ru">Craig JC, Williams GJ, Jones M, et al. The accuracy of clinical symptoms and signs for the diagnosis of serious bacterial infection in young febrile children: prospective cohort study of 15 781 febrile illnesses. BMJ. 2010;340:c1594. doi: https://doi.org/10.1136/bmj.c1594</mixed-citation><mixed-citation xml:lang="en">Craig JC, Williams GJ, Jones M, et al. The accuracy of clinical symptoms and signs for the diagnosis of serious bacterial infection in young febrile children: prospective cohort study of 15 781 febrile illnesses. BMJ. 2010;340:c1594. doi: https://doi.org/10.1136/bmj.c1594</mixed-citation></citation-alternatives></ref><ref id="cit212"><label>212</label><citation-alternatives><mixed-citation xml:lang="ru">Todenhofer T, Renninger M, Schwentner C, et al. A new prognostic model for cancer-specific survival after radical cystectomy including pretreatment thrombocytosis and standard pathological risk factors. BJU Int. 2012;110(11 Pt B):E533–E540. doi: https://doi.org/10.1111/j.1464-410X.2012.11231.x</mixed-citation><mixed-citation xml:lang="en">Todenhofer T, Renninger M, Schwentner C, et al. A new prognostic model for cancer-specific survival after radical cystectomy including pretreatment thrombocytosis and standard pathological risk factors. BJU Int. 2012;110(11 Pt B):E533–E540. doi: https://doi.org/10.1111/j.1464-410X.2012.11231.x</mixed-citation></citation-alternatives></ref><ref id="cit213"><label>213</label><citation-alternatives><mixed-citation xml:lang="ru">Boggs DA, Rosenberg L, Pencina MJ, et al. Validation of a breast cancer risk prediction model developed for Black women. J Natl Cancer Inst. 2013;105(5):361–367. doi: https://doi.org/10.1093/jnci/djt008</mixed-citation><mixed-citation xml:lang="en">Boggs DA, Rosenberg L, Pencina MJ, et al. Validation of a breast cancer risk prediction model developed for Black women. J Natl Cancer Inst. 2013;105(5):361–367. doi: https://doi.org/10.1093/jnci/djt008</mixed-citation></citation-alternatives></ref><ref id="cit214"><label>214</label><citation-alternatives><mixed-citation xml:lang="ru">Knottnerus JA, Buntinx F. The Evidence Base of Clinical Diagnosis: Theory and Methods of Diagnostic Research. Hoboken, NJ: Wiley-Blackwell; 2009.</mixed-citation><mixed-citation xml:lang="en">Knottnerus JA, Buntinx F. The Evidence Base of Clinical Diagnosis: Theory and Methods of Diagnostic Research. Hoboken, NJ: Wiley-Blackwell; 2009.</mixed-citation></citation-alternatives></ref><ref id="cit215"><label>215</label><citation-alternatives><mixed-citation xml:lang="ru">Naaktgeboren CA, de Groot JA, van Smeden M, et al. Evaluating diagnostic accuracy in the face of multiple reference standards. Ann Intern Med. 2013;159(3):195–202. doi: https://doi.org/10.7326/0003-4819-159-3-201308060-00009</mixed-citation><mixed-citation xml:lang="en">Naaktgeboren CA, de Groot JA, van Smeden M, et al. Evaluating diagnostic accuracy in the face of multiple reference standards. Ann Intern Med. 2013;159(3):195–202. doi: https://doi.org/10.7326/0003-4819-159-3-201308060-00009</mixed-citation></citation-alternatives></ref><ref id="cit216"><label>216</label><citation-alternatives><mixed-citation xml:lang="ru">Bertens LC, Broekhuizen BD, Naaktgeboren CA, et al. Use of expert panels to define the reference standard in diagnostic research: a systematic review of published methods and reporting. PLoS Med. 2013;10(10):e1001531. doi: https://doi.org/10.1371/journal.pmed.1001531</mixed-citation><mixed-citation xml:lang="en">Bertens LC, Broekhuizen BD, Naaktgeboren CA, et al. Use of expert panels to define the reference standard in diagnostic research: a systematic review of published methods and reporting. PLoS Med. 2013;10(10):e1001531. doi: https://doi.org/10.1371/journal.pmed.1001531</mixed-citation></citation-alternatives></ref><ref id="cit217"><label>217</label><citation-alternatives><mixed-citation xml:lang="ru">Naaktgeboren CA, Bertens LC, van Smeden M, et al. Value of composite reference standards in diagnostic research. BMJ. 2013; 347:f5605. doi: https://doi.org/10.1136/bmj.f5605</mixed-citation><mixed-citation xml:lang="en">Naaktgeboren CA, Bertens LC, van Smeden M, et al. Value of composite reference standards in diagnostic research. BMJ. 2013; 347:f5605. doi: https://doi.org/10.1136/bmj.f5605</mixed-citation></citation-alternatives></ref><ref id="cit218"><label>218</label><citation-alternatives><mixed-citation xml:lang="ru">de Groot JA, Bossuyt PM, Reitsma JB, et al. Verification problems in diagnostic accuracy studies: consequences and solutions. BMJ. 2011;343:d4770. doi: https://doi.org/10.1136/bmj.d4770</mixed-citation><mixed-citation xml:lang="en">de Groot JA, Bossuyt PM, Reitsma JB, et al. Verification problems in diagnostic accuracy studies: consequences and solutions. BMJ. 2011;343:d4770. doi: https://doi.org/10.1136/bmj.d4770</mixed-citation></citation-alternatives></ref><ref id="cit219"><label>219</label><citation-alternatives><mixed-citation xml:lang="ru">de Groot JA, Dendukuri N, Janssen KJ, et al. Adjusting for partial verification or workup bias in meta-analyses of diagnostic accuracy studies. Am J Epidemiol. 2012;175(8):847–853. doi: https://doi.org/10.1093/aje/kwr383</mixed-citation><mixed-citation xml:lang="en">de Groot JA, Dendukuri N, Janssen KJ, et al. Adjusting for partial verification or workup bias in meta-analyses of diagnostic accuracy studies. Am J Epidemiol. 2012;175(8):847–853. doi: https://doi.org/10.1093/aje/kwr383</mixed-citation></citation-alternatives></ref><ref id="cit220"><label>220</label><citation-alternatives><mixed-citation xml:lang="ru">Rutjes AW, Reitsma JB, DiNisio M, et al. Evidence of bias and variation in diagnostic accuracy studies. CMAJ. 2006;174(4): 469–476. doi: https://doi.org/10.1503/cmaj.050090</mixed-citation><mixed-citation xml:lang="en">Rutjes AW, Reitsma JB, DiNisio M, et al. Evidence of bias and variation in diagnostic accuracy studies. CMAJ. 2006;174(4): 469–476. doi: https://doi.org/10.1503/cmaj.050090</mixed-citation></citation-alternatives></ref><ref id="cit221"><label>221</label><citation-alternatives><mixed-citation xml:lang="ru">Rouzier R, Pusztai L, Delaloge S, et al. Nomograms to predict pathologic complete response and metastasis-free survival after preoperative chemotherapy for breast cancer. J Clin Oncol. 2005;23(33):8331–8339. doi: https://doi.org/10.1200/JCO.2005.01.2898</mixed-citation><mixed-citation xml:lang="en">Rouzier R, Pusztai L, Delaloge S, et al. Nomograms to predict pathologic complete response and metastasis-free survival after preoperative chemotherapy for breast cancer. J Clin Oncol. 2005;23(33):8331–8339. doi: https://doi.org/10.1200/JCO.2005.01.2898</mixed-citation></citation-alternatives></ref><ref id="cit222"><label>222</label><citation-alternatives><mixed-citation xml:lang="ru">Elliott J, Beringer T, Kee F, et al. Predicting survival after treatment for fracture of the proximal femur and the effect of delays to surgery. J Clin Epidemiol. 2003;56(8):788–795. doi: https://doi.org/10.1016/s08954356(03)00129-x</mixed-citation><mixed-citation xml:lang="en">Elliott J, Beringer T, Kee F, et al. Predicting survival after treatment for fracture of the proximal femur and the effect of delays to surgery. J Clin Epidemiol. 2003;56(8):788–795. doi: https://doi.org/10.1016/s08954356(03)00129-x</mixed-citation></citation-alternatives></ref><ref id="cit223"><label>223</label><citation-alternatives><mixed-citation xml:lang="ru">Adams LA, Bulsara M, Rossi E, et al. Hepascore: an accurate validated predictor of liver fibrosis in chronic hepatitis C infection. Clin Chem. 2005;51(10):1867–1873. doi: https://doi.org/10.1373/clinchem.2005.048389</mixed-citation><mixed-citation xml:lang="en">Adams LA, Bulsara M, Rossi E, et al. Hepascore: an accurate validated predictor of liver fibrosis in chronic hepatitis C infection. Clin Chem. 2005;51(10):1867–1873. doi: https://doi.org/10.1373/clinchem.2005.048389</mixed-citation></citation-alternatives></ref><ref id="cit224"><label>224</label><citation-alternatives><mixed-citation xml:lang="ru">Hess EP, Brison RJ, Perry JJ, et al. Development of a clinical prediction rule for 30-day cardiac events in emergency department patients with chest pain and possible acute coronary syndrome. Ann Emerg Med. 2012;59(2):115–125. doi: https://doi.org/10.1016/j.annemergmed.2011.07.026</mixed-citation><mixed-citation xml:lang="en">Hess EP, Brison RJ, Perry JJ, et al. Development of a clinical prediction rule for 30-day cardiac events in emergency department patients with chest pain and possible acute coronary syndrome. Ann Emerg Med. 2012;59(2):115–125. doi: https://doi.org/10.1016/j.annemergmed.2011.07.026</mixed-citation></citation-alternatives></ref><ref id="cit225"><label>225</label><citation-alternatives><mixed-citation xml:lang="ru">Moons KG, Grobbee DE. When should we remain blind and when should our eyes remain open in diagnostic studies? J Clin Epidemiol. 2002;55(7):633–636. doi: https://doi.org/10.1016/s0895-4356(02)00408-0</mixed-citation><mixed-citation xml:lang="en">Moons KG, Grobbee DE. When should we remain blind and when should our eyes remain open in diagnostic studies? J Clin Epidemiol. 2002;55(7):633–636. doi: https://doi.org/10.1016/s0895-4356(02)00408-0</mixed-citation></citation-alternatives></ref><ref id="cit226"><label>226</label><citation-alternatives><mixed-citation xml:lang="ru">Rutjes AW, Reitsma JB, Coomarasamy A, et al. Evaluation of diagnostic tests when there is no gold standard. A review of methods. Health Technol Assess. 2007;11(50):iii, ix–51. doi: https://doi.org/10.3310/hta11500</mixed-citation><mixed-citation xml:lang="en">Rutjes AW, Reitsma JB, Coomarasamy A, et al. Evaluation of diagnostic tests when there is no gold standard. A review of methods. Health Technol Assess. 2007;11(50):iii, ix–51. doi: https://doi.org/10.3310/hta11500</mixed-citation></citation-alternatives></ref><ref id="cit227"><label>227</label><citation-alternatives><mixed-citation xml:lang="ru">Kaijser J, Sayasneh A, Van Hoorde K, et al. Presurgical diagnosis of adnexal tumours using mathematical models and scoring systems: a systematic review and meta-analysis. Hum Reprod Update. 2014;20(3):449–452. doi: https://doi.org/10.1093/humupd/dmt059</mixed-citation><mixed-citation xml:lang="en">Kaijser J, Sayasneh A, Van Hoorde K, et al. Presurgical diagnosis of adnexal tumours using mathematical models and scoring systems: a systematic review and meta-analysis. Hum Reprod Update. 2014;20(3):449–452. doi: https://doi.org/10.1093/humupd/dmt059</mixed-citation></citation-alternatives></ref><ref id="cit228"><label>228</label><citation-alternatives><mixed-citation xml:lang="ru">Kaul V, Friedenberg FK, Braitman LE, et al. Development and validation of a model to diagnose cirrhosis in patients with hepatitis C. Am J Gastroenterol. 2002;97(10):2623–2628. doi: https://doi.org/10.1111/j.1572-0241.2002.06040.x</mixed-citation><mixed-citation xml:lang="en">Kaul V, Friedenberg FK, Braitman LE, et al. Development and validation of a model to diagnose cirrhosis in patients with hepatitis C. Am J Gastroenterol. 2002;97(10):2623–2628. doi: https://doi.org/10.1111/j.1572-0241.2002.06040.x</mixed-citation></citation-alternatives></ref><ref id="cit229"><label>229</label><citation-alternatives><mixed-citation xml:lang="ru">Halbesma N, Jansen DF, Heymans MW, et al. Development and validation of a general population renal risk score. Clin J Am Soc Nephrol. 2011;6(7):1731–1738. doi: https://doi.org/10.2215/CJN.08590910</mixed-citation><mixed-citation xml:lang="en">Halbesma N, Jansen DF, Heymans MW, et al. Development and validation of a general population renal risk score. Clin J Am Soc Nephrol. 2011;6(7):1731–1738. doi: https://doi.org/10.2215/CJN.08590910</mixed-citation></citation-alternatives></ref><ref id="cit230"><label>230</label><citation-alternatives><mixed-citation xml:lang="ru">Beyersmann J, Wolkewitz M, Schumacher M. The impact of time-dependent bias in proportional hazards modelling. Stat Med. 2008;27(30):6439–6454. doi: https://doi.org/10.1002/sim.3437</mixed-citation><mixed-citation xml:lang="en">Beyersmann J, Wolkewitz M, Schumacher M. The impact of time-dependent bias in proportional hazards modelling. Stat Med. 2008;27(30):6439–6454. doi: https://doi.org/10.1002/sim.3437</mixed-citation></citation-alternatives></ref><ref id="cit231"><label>231</label><citation-alternatives><mixed-citation xml:lang="ru">van Walraven C, Davis D, Forster AJ, Wells GA. Time-dependent bias was common in survival analyses published in leading clinical journals. J Clin Epidemiol. 2004;57(7):672–682. doi: https://doi.org/10.1016/j.jclinepi.2003.12.008</mixed-citation><mixed-citation xml:lang="en">van Walraven C, Davis D, Forster AJ, Wells GA. Time-dependent bias was common in survival analyses published in leading clinical journals. J Clin Epidemiol. 2004;57(7):672–682. doi: https://doi.org/10.1016/j.jclinepi.2003.12.008</mixed-citation></citation-alternatives></ref><ref id="cit232"><label>232</label><citation-alternatives><mixed-citation xml:lang="ru">Rochon J. Issues in adjusting for covariates arising postrandomization in clinical trials. Drug Inf J. 1999;33:1219–1228.</mixed-citation><mixed-citation xml:lang="en">Rochon J. Issues in adjusting for covariates arising postrandomization in clinical trials. Drug Inf J. 1999;33:1219–1228.</mixed-citation></citation-alternatives></ref><ref id="cit233"><label>233</label><citation-alternatives><mixed-citation xml:lang="ru">D’Agostino RB. Beyond baseline data: the use of time-varying covariates. J Hypertens. 2008;26(4):639–640. doi: https://doi.org/10.1097/HJH.0b013e3282fcbc22</mixed-citation><mixed-citation xml:lang="en">D’Agostino RB. Beyond baseline data: the use of time-varying covariates. J Hypertens. 2008;26(4):639–640. doi: https://doi.org/10.1097/HJH.0b013e3282fcbc22</mixed-citation></citation-alternatives></ref><ref id="cit234"><label>234</label><citation-alternatives><mixed-citation xml:lang="ru">Scheike TH. Time-varying effects in survival analysis. In: Advances in Survival Analysis. Rao CR, ed. Amsterdam: Elsevier; 2004. pp 61–68.</mixed-citation><mixed-citation xml:lang="en">Scheike TH. Time-varying effects in survival analysis. In: Advances in Survival Analysis. Rao CR, ed. Amsterdam: Elsevier; 2004. pp 61–68.</mixed-citation></citation-alternatives></ref><ref id="cit235"><label>235</label><citation-alternatives><mixed-citation xml:lang="ru">Sun GW, Shook TL, Kay GL. Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol. 1996;49(8):907–916. doi: https://doi.org/10.1016/0895-4356(96)00025-x</mixed-citation><mixed-citation xml:lang="en">Sun GW, Shook TL, Kay GL. Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol. 1996;49(8):907–916. doi: https://doi.org/10.1016/0895-4356(96)00025-x</mixed-citation></citation-alternatives></ref><ref id="cit236"><label>236</label><citation-alternatives><mixed-citation xml:lang="ru">Rutten FH, Vonken EJ, Cramer MJ, et al. Cardiovascular magnetic resonance imaging to identify left-sided chronic heart failure in stable patients with chronic obstructive pulmonary disease. Am Heart J. 2008;156(3):506–512. doi: https://doi.org/10.1016/j.ahj.2008.04.021</mixed-citation><mixed-citation xml:lang="en">Rutten FH, Vonken EJ, Cramer MJ, et al. Cardiovascular magnetic resonance imaging to identify left-sided chronic heart failure in stable patients with chronic obstructive pulmonary disease. Am Heart J. 2008;156(3):506–512. doi: https://doi.org/10.1016/j.ahj.2008.04.021</mixed-citation></citation-alternatives></ref><ref id="cit237"><label>237</label><citation-alternatives><mixed-citation xml:lang="ru">Hess EP, Perry JJ, Calder LA, et al. Prospective validation of a modified thrombolysis in myocardial infarction risk score in emergency department patients with chest pain and possible acute coronary syndrome. Acad Emerg Med. 2010;17(4):368–375. doi: https://doi.org/10.1111/j.1553-2712.2010.00696.x</mixed-citation><mixed-citation xml:lang="en">Hess EP, Perry JJ, Calder LA, et al. Prospective validation of a modified thrombolysis in myocardial infarction risk score in emergency department patients with chest pain and possible acute coronary syndrome. Acad Emerg Med. 2010;17(4):368–375. doi: https://doi.org/10.1111/j.1553-2712.2010.00696.x</mixed-citation></citation-alternatives></ref><ref id="cit238"><label>238</label><citation-alternatives><mixed-citation xml:lang="ru">Begg CB. Biases in the assessment of diagnostic tests. Stat Med. 1987;6(4):411–423. doi: https://doi.org/10.1002/sim.4780060402</mixed-citation><mixed-citation xml:lang="en">Begg CB. Biases in the assessment of diagnostic tests. Stat Med. 1987;6(4):411–423. doi: https://doi.org/10.1002/sim.4780060402</mixed-citation></citation-alternatives></ref><ref id="cit239"><label>239</label><citation-alternatives><mixed-citation xml:lang="ru">Elmore JG, Wells CK, Howard DH, Feinstein AR. The impact of clinical history on mammographic interpretations. JAMA. 1997; 277(1):49–52.</mixed-citation><mixed-citation xml:lang="en">Elmore JG, Wells CK, Howard DH, Feinstein AR. The impact of clinical history on mammographic interpretations. JAMA. 1997; 277(1):49–52.</mixed-citation></citation-alternatives></ref><ref id="cit240"><label>240</label><citation-alternatives><mixed-citation xml:lang="ru">Loy CT, Irwig L. Accuracy of diagnostic tests read with and without clinical information: a systematic review. JAMA. 2004;292(13):1602–1609. doi: https://doi.org/10.1001/jama.292.13.1602</mixed-citation><mixed-citation xml:lang="en">Loy CT, Irwig L. Accuracy of diagnostic tests read with and without clinical information: a systematic review. JAMA. 2004;292(13):1602–1609. doi: https://doi.org/10.1001/jama.292.13.1602</mixed-citation></citation-alternatives></ref><ref id="cit241"><label>241</label><citation-alternatives><mixed-citation xml:lang="ru">Loewen P, Dahir K. Risk of bleeding with oral anticoagulants: an updated systematic review and performance analysis of clinical prediction rules. Ann Hematol. 2011;90(10):1191–1200. doi: https://doi.org/10.1007/s00277-011-1267-3</mixed-citation><mixed-citation xml:lang="en">Loewen P, Dahir K. Risk of bleeding with oral anticoagulants: an updated systematic review and performance analysis of clinical prediction rules. Ann Hematol. 2011;90(10):1191–1200. doi: https://doi.org/10.1007/s00277-011-1267-3</mixed-citation></citation-alternatives></ref><ref id="cit242"><label>242</label><citation-alternatives><mixed-citation xml:lang="ru">Sheth T, Butler C, Chow B, et al. The coronary CT angiography vision protocol: a prospective observational imaging cohort study in patients undergoing non-cardiac surgery. BMJ Open. 2012;2(4):e001474. doi: https://doi.org/10.1136/bmjopen-2012-001474</mixed-citation><mixed-citation xml:lang="en">Sheth T, Butler C, Chow B, et al. The coronary CT angiography vision protocol: a prospective observational imaging cohort study in patients undergoing non-cardiac surgery. BMJ Open. 2012;2(4):e001474. doi: https://doi.org/10.1136/bmjopen-2012-001474</mixed-citation></citation-alternatives></ref><ref id="cit243"><label>243</label><citation-alternatives><mixed-citation xml:lang="ru">Hippisley-Cox J, Coupland C. Identifying patients with suspected pancreatic cancer in primary care: derivation and validation of an algorithm. Br J Gen Pract. 2012;62(594):e38–e45. doi: https://doi.org/10.3399/bjgp12X616355</mixed-citation><mixed-citation xml:lang="en">Hippisley-Cox J, Coupland C. Identifying patients with suspected pancreatic cancer in primary care: derivation and validation of an algorithm. Br J Gen Pract. 2012;62(594):e38–e45. doi: https://doi.org/10.3399/bjgp12X616355</mixed-citation></citation-alternatives></ref><ref id="cit244"><label>244</label><citation-alternatives><mixed-citation xml:lang="ru">Holmes JF, Mao A, Awasthi S, et al. Validation of a prediction rule for the identification of children with intra-abdominal injuries after blunt torso trauma. Ann Emerg Med. 2009;54(4):528–533. doi: https://doi.org/10.1016/j.annemergmed.2009.01.019</mixed-citation><mixed-citation xml:lang="en">Holmes JF, Mao A, Awasthi S, et al. Validation of a prediction rule for the identification of children with intra-abdominal injuries after blunt torso trauma. Ann Emerg Med. 2009;54(4):528–533. doi: https://doi.org/10.1016/j.annemergmed.2009.01.019</mixed-citation></citation-alternatives></ref><ref id="cit245"><label>245</label><citation-alternatives><mixed-citation xml:lang="ru">Peduzzi P, Concato J, Feinstein AR, Holford TR. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol. 1995;48(12):1503–1512. doi: https://doi.org/10.1016/0895-4356(95)00048-8</mixed-citation><mixed-citation xml:lang="en">Peduzzi P, Concato J, Feinstein AR, Holford TR. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol. 1995;48(12):1503–1512. doi: https://doi.org/10.1016/0895-4356(95)00048-8</mixed-citation></citation-alternatives></ref><ref id="cit246"><label>246</label><citation-alternatives><mixed-citation xml:lang="ru">Peduzzi P, Concato J, Kemper E, et al. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373–1379. doi: https://doi.org/10.1016/s0895-4356(96)00236-3</mixed-citation><mixed-citation xml:lang="en">Peduzzi P, Concato J, Kemper E, et al. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373–1379. doi: https://doi.org/10.1016/s0895-4356(96)00236-3</mixed-citation></citation-alternatives></ref><ref id="cit247"><label>247</label><citation-alternatives><mixed-citation xml:lang="ru">Vittinghoff E, McCulloch CE. Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol. 2007;165(6):710–718. doi: https://doi.org/10.1093/aje/kwk052</mixed-citation><mixed-citation xml:lang="en">Vittinghoff E, McCulloch CE. Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol. 2007;165(6):710–718. doi: https://doi.org/10.1093/aje/kwk052</mixed-citation></citation-alternatives></ref><ref id="cit248"><label>248</label><citation-alternatives><mixed-citation xml:lang="ru">Feinstein AR. Multivariable Analysis. New Haven, CT: Yale University Press; 1996.</mixed-citation><mixed-citation xml:lang="en">Feinstein AR. Multivariable Analysis. New Haven, CT: Yale University Press; 1996.</mixed-citation></citation-alternatives></ref><ref id="cit249"><label>249</label><citation-alternatives><mixed-citation xml:lang="ru">Schumacher M, Holländer N, Schwarzer G, et al. Prognostic factor studies. In: Handbook of Statistics in Clinical Oncology. Crowley J, Hoering A, eds. 3rd ed. London: Chapman and Hall/CRC; 2012. pp. 415–470.</mixed-citation><mixed-citation xml:lang="en">Schumacher M, Holländer N, Schwarzer G, et al. Prognostic factor studies. In: Handbook of Statistics in Clinical Oncology. Crowley J, Hoering A, eds. 3rd ed. London: Chapman and Hall/CRC; 2012. pp. 415–470.</mixed-citation></citation-alternatives></ref><ref id="cit250"><label>250</label><citation-alternatives><mixed-citation xml:lang="ru">Courvoisier DS, Combescure C, Agoritsas T, et al. Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure. J Clin Epidemiol. 2011;64(9): 993–1000. doi: https://doi.org/10.1016/j.jclinepi.2010.11.012</mixed-citation><mixed-citation xml:lang="en">Courvoisier DS, Combescure C, Agoritsas T, et al. Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure. J Clin Epidemiol. 2011;64(9): 993–1000. doi: https://doi.org/10.1016/j.jclinepi.2010.11.012</mixed-citation></citation-alternatives></ref><ref id="cit251"><label>251</label><citation-alternatives><mixed-citation xml:lang="ru">Jinks RC. Sample size for multivariable prognostic models. PhD thesis. University College London; 2012.</mixed-citation><mixed-citation xml:lang="en">Jinks RC. Sample size for multivariable prognostic models. PhD thesis. University College London; 2012.</mixed-citation></citation-alternatives></ref><ref id="cit252"><label>252</label><citation-alternatives><mixed-citation xml:lang="ru">Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21(1):128–138. doi: https://doi.org/10.1097/EDE.0b013e3181c30fb2</mixed-citation><mixed-citation xml:lang="en">Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21(1):128–138. doi: https://doi.org/10.1097/EDE.0b013e3181c30fb2</mixed-citation></citation-alternatives></ref><ref id="cit253"><label>253</label><citation-alternatives><mixed-citation xml:lang="ru">Steyerberg EW, Calster BV, Pencina MJ. Performance measures for prediction models and markers: evaluation of predictions and classifications. Rev Esp Cardiol (Engl Ed). 2011;64(9):788–794. doi: https://doi.org/10.1016/j.recesp.2011.04.017</mixed-citation><mixed-citation xml:lang="en">Steyerberg EW, Calster BV, Pencina MJ. Performance measures for prediction models and markers: evaluation of predictions and classifications. Rev Esp Cardiol (Engl Ed). 2011;64(9):788–794. doi: https://doi.org/10.1016/j.recesp.2011.04.017</mixed-citation></citation-alternatives></ref><ref id="cit254"><label>254</label><citation-alternatives><mixed-citation xml:lang="ru">Vergouwe Y, Steyerberg EW, Eijkemans MJ, Habbema JD. Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. J Clin Epidemiol. 2005;58(5):475–483. doi: https://doi.org/10.1016/j.jclinepi.2004.06.017</mixed-citation><mixed-citation xml:lang="en">Vergouwe Y, Steyerberg EW, Eijkemans MJ, Habbema JD. Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. J Clin Epidemiol. 2005;58(5):475–483. doi: https://doi.org/10.1016/j.jclinepi.2004.06.017</mixed-citation></citation-alternatives></ref><ref id="cit255"><label>255</label><citation-alternatives><mixed-citation xml:lang="ru">Audigé L, Bhandari M, Kellam J. How reliable are reliability studies of fracture classifications? A systematic review of their methodologies. Acta Orthop Scand. 2004;75(2):184–194. doi: https://doi.org/10.1080/00016470412331294445</mixed-citation><mixed-citation xml:lang="en">Audigé L, Bhandari M, Kellam J. How reliable are reliability studies of fracture classifications? A systematic review of their methodologies. Acta Orthop Scand. 2004;75(2):184–194. doi: https://doi.org/10.1080/00016470412331294445</mixed-citation></citation-alternatives></ref><ref id="cit256"><label>256</label><citation-alternatives><mixed-citation xml:lang="ru">Genders TS, Steyerberg EW, Hunink MG, et al. Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts. BMJ. 2012;344:e3485. doi: https://doi.org/10.1136/bmj.e3485</mixed-citation><mixed-citation xml:lang="en">Genders TS, Steyerberg EW, Hunink MG, et al. Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts. BMJ. 2012;344:e3485. doi: https://doi.org/10.1136/bmj.e3485</mixed-citation></citation-alternatives></ref><ref id="cit257"><label>257</label><citation-alternatives><mixed-citation xml:lang="ru">Thompson DO, Hurtado TR, Liao MM, et al. Validation of the Simplified Motor Score in the out-ofhospital setting for the prediction of outcomes after traumatic brain injury. Ann Emerg Med. 2011;58(5):417–425. doi: https://doi.org/10.1016/j.annemergmed.2011.05.033</mixed-citation><mixed-citation xml:lang="en">Thompson DO, Hurtado TR, Liao MM, et al. Validation of the Simplified Motor Score in the out-ofhospital setting for the prediction of outcomes after traumatic brain injury. Ann Emerg Med. 2011;58(5):417–425. doi: https://doi.org/10.1016/j.annemergmed.2011.05.033</mixed-citation></citation-alternatives></ref><ref id="cit258"><label>258</label><citation-alternatives><mixed-citation xml:lang="ru">Ambler G, Omar RZ, Royston P, et al. Generic, simple risk stratification model for heart valve surgery. Circulation. 2005;112(2):224–231. doi: https://doi.org/10.1161/CIRCULATIONAHA.104.515049</mixed-citation><mixed-citation xml:lang="en">Ambler G, Omar RZ, Royston P, et al. Generic, simple risk stratification model for heart valve surgery. Circulation. 2005;112(2):224–231. doi: https://doi.org/10.1161/CIRCULATIONAHA.104.515049</mixed-citation></citation-alternatives></ref><ref id="cit259"><label>259</label><citation-alternatives><mixed-citation xml:lang="ru">Mackinnon A. The use and reporting of multiple imputation in medical research — a review. J Intern Med. 2010;268(6):586–593. doi: https://doi.org/10.1111/j.1365-2796.2010.02274.x</mixed-citation><mixed-citation xml:lang="en">Mackinnon A. The use and reporting of multiple imputation in medical research — a review. J Intern Med. 2010;268(6):586–593. doi: https://doi.org/10.1111/j.1365-2796.2010.02274.x</mixed-citation></citation-alternatives></ref><ref id="cit260"><label>260</label><citation-alternatives><mixed-citation xml:lang="ru">Hussain A, Dunn KW. Predicting length of stay in thermal burns: a systematic review of prognostic factors. Burns. 2013;39(7): 1331–1340. doi: https://doi.org/10.1016/j.burns.2013.04.026</mixed-citation><mixed-citation xml:lang="en">Hussain A, Dunn KW. Predicting length of stay in thermal burns: a systematic review of prognostic factors. Burns. 2013;39(7): 1331–1340. doi: https://doi.org/10.1016/j.burns.2013.04.026</mixed-citation></citation-alternatives></ref><ref id="cit261"><label>261</label><citation-alternatives><mixed-citation xml:lang="ru">Tangri N, Stevens LA, Griffith J, et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA. 2011;305(15):1553–1559. doi: https://doi.org/10.1001/jama.2011.451</mixed-citation><mixed-citation xml:lang="en">Tangri N, Stevens LA, Griffith J, et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA. 2011;305(15):1553–1559. doi: https://doi.org/10.1001/jama.2011.451</mixed-citation></citation-alternatives></ref><ref id="cit262"><label>262</label><citation-alternatives><mixed-citation xml:lang="ru">Steyerberg EW, Mushkudiani N, Perel P, et al. Predicting outcome after traumatic brain injury: development and internatio nal validation of prognostic scores based on admission characteristics. PLoS Med. 2008;5(8):e165. doi: https://doi.org/10.1371/journal.pmed.0050165</mixed-citation><mixed-citation xml:lang="en">Steyerberg EW, Mushkudiani N, Perel P, et al. Predicting outcome after traumatic brain injury: development and internatio nal validation of prognostic scores based on admission characteristics. PLoS Med. 2008;5(8):e165. doi: https://doi.org/10.1371/journal.pmed.0050165</mixed-citation></citation-alternatives></ref><ref id="cit263"><label>263</label><citation-alternatives><mixed-citation xml:lang="ru">Tammemagi CM, Pinsky PF, Caporaso NE, et al. Lung cancer risk prediction: Prostate, Lung, Colorectal And Ovarian Cancer Screening Trial models and validation. J Natl Cancer Inst. 2011;103(13): 1058–1068. doi: https://doi.org/10.1093/jnci/djr173</mixed-citation><mixed-citation xml:lang="en">Tammemagi CM, Pinsky PF, Caporaso NE, et al. Lung cancer risk prediction: Prostate, Lung, Colorectal And Ovarian Cancer Screening Trial models and validation. J Natl Cancer Inst. 2011;103(13): 1058–1068. doi: https://doi.org/10.1093/jnci/djr173</mixed-citation></citation-alternatives></ref><ref id="cit264"><label>264</label><citation-alternatives><mixed-citation xml:lang="ru">Altman DG, Lausen B, Sauerbrei W, Schumacher M. Dangers of using “optimal” cutpoints in the evaluation of prognostic factors. J Natl Cancer Inst. 1994;86(11):829–835. doi: https://doi.org/10.1093/jnci/86.11.829</mixed-citation><mixed-citation xml:lang="en">Altman DG, Lausen B, Sauerbrei W, Schumacher M. Dangers of using “optimal” cutpoints in the evaluation of prognostic factors. J Natl Cancer Inst. 1994;86(11):829–835. doi: https://doi.org/10.1093/jnci/86.11.829</mixed-citation></citation-alternatives></ref><ref id="cit265"><label>265</label><citation-alternatives><mixed-citation xml:lang="ru">Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med. 2006;25(1):127–141. doi: https://doi.org/10.1002/sim.2331</mixed-citation><mixed-citation xml:lang="en">Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med. 2006;25(1):127–141. doi: https://doi.org/10.1002/sim.2331</mixed-citation></citation-alternatives></ref><ref id="cit266"><label>266</label><citation-alternatives><mixed-citation xml:lang="ru">Royston P, Sauerbrei W. Multivariable Model-Building: A Pragmatic Approach to Regression Analysis Based on Fractional Polynomials for Modelling Continuous Variables. Chichester: John Wiley; 2008.</mixed-citation><mixed-citation xml:lang="en">Royston P, Sauerbrei W. Multivariable Model-Building: A Pragmatic Approach to Regression Analysis Based on Fractional Polynomials for Modelling Continuous Variables. Chichester: John Wiley; 2008.</mixed-citation></citation-alternatives></ref><ref id="cit267"><label>267</label><citation-alternatives><mixed-citation xml:lang="ru">Veerbeek JM, Kwakkel G, van Wegen EE, et al. Early prediction of outcome of activities of daily living after stroke: a systematic review. Stroke. 2011;42(5):1482–1488. doi: https://doi.org/10.1161/STROKEAHA.110.604090</mixed-citation><mixed-citation xml:lang="en">Veerbeek JM, Kwakkel G, van Wegen EE, et al. Early prediction of outcome of activities of daily living after stroke: a systematic review. Stroke. 2011;42(5):1482–1488. doi: https://doi.org/10.1161/STROKEAHA.110.604090</mixed-citation></citation-alternatives></ref><ref id="cit268"><label>268</label><citation-alternatives><mixed-citation xml:lang="ru">Lubetzky-Vilnai A, Ciol M, McCoy SW. Statistical analysis of clinical prediction rules for rehabilitation interventions: current state of the literature. Arch Phys Med Rehabil. 2014;95(1):188–196. doi: https://doi.org/10.1016/j.apmr.2013.08.242</mixed-citation><mixed-citation xml:lang="en">Lubetzky-Vilnai A, Ciol M, McCoy SW. Statistical analysis of clinical prediction rules for rehabilitation interventions: current state of the literature. Arch Phys Med Rehabil. 2014;95(1):188–196. doi: https://doi.org/10.1016/j.apmr.2013.08.242</mixed-citation></citation-alternatives></ref><ref id="cit269"><label>269</label><citation-alternatives><mixed-citation xml:lang="ru">Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J. 2014;35(29):1925–1931. doi: https://doi.org/10.1093/eurheartj/ehu207</mixed-citation><mixed-citation xml:lang="en">Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J. 2014;35(29):1925–1931. doi: https://doi.org/10.1093/eurheartj/ehu207</mixed-citation></citation-alternatives></ref><ref id="cit270"><label>270</label><citation-alternatives><mixed-citation xml:lang="ru">Ioannidis JP. Why most discovered true associations are inflated. Epidemiology. 2008;19(5):640–648. doi: https://doi.org/10.1097/EDE.0b013e31818131e7</mixed-citation><mixed-citation xml:lang="en">Ioannidis JP. Why most discovered true associations are inflated. Epidemiology. 2008;19(5):640–648. doi: https://doi.org/10.1097/EDE.0b013e31818131e7</mixed-citation></citation-alternatives></ref><ref id="cit271"><label>271</label><citation-alternatives><mixed-citation xml:lang="ru">Hrynaszkiewicz I, Norton ML, Vickers AJ, Altman DG. Preparing raw clinical data for publication: guidance for journal editors, authors, and peer reviewers. Trials. 2010;11:9. doi: https://doi.org/10.1186/1745-6215-11-9</mixed-citation><mixed-citation xml:lang="en">Hrynaszkiewicz I, Norton ML, Vickers AJ, Altman DG. Preparing raw clinical data for publication: guidance for journal editors, authors, and peer reviewers. Trials. 2010;11:9. doi: https://doi.org/10.1186/1745-6215-11-9</mixed-citation></citation-alternatives></ref><ref id="cit272"><label>272</label><citation-alternatives><mixed-citation xml:lang="ru">Hosmer DW, Lemeshow S. Applied Logistic Regression. New York: Wiley; 2000.</mixed-citation><mixed-citation xml:lang="en">Hosmer DW, Lemeshow S. Applied Logistic Regression. New York: Wiley; 2000.</mixed-citation></citation-alternatives></ref><ref id="cit273"><label>273</label><citation-alternatives><mixed-citation xml:lang="ru">Vittinghoff E. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. New York: Springer; 2005.</mixed-citation><mixed-citation xml:lang="en">Vittinghoff E. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. New York: Springer; 2005.</mixed-citation></citation-alternatives></ref><ref id="cit274"><label>274</label><citation-alternatives><mixed-citation xml:lang="ru">Hosmer DW, Lemeshow S, May S. Applied Survival Analysis: Regression Modelling of Time-To-Event Data. Hoboken, NJ: WileyInterscience; 2008.</mixed-citation><mixed-citation xml:lang="en">Hosmer DW, Lemeshow S, May S. Applied Survival Analysis: Regression Modelling of Time-To-Event Data. Hoboken, NJ: WileyInterscience; 2008.</mixed-citation></citation-alternatives></ref><ref id="cit275"><label>275</label><citation-alternatives><mixed-citation xml:lang="ru">Hastie T, Tibshirani R, Friedman JH. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer; 2001.</mixed-citation><mixed-citation xml:lang="en">Hastie T, Tibshirani R, Friedman JH. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer; 2001.</mixed-citation></citation-alternatives></ref><ref id="cit276"><label>276</label><citation-alternatives><mixed-citation xml:lang="ru">Kuhn M, Johnson K. Applied Predictive Modelling. New York: Springer; 2013.</mixed-citation><mixed-citation xml:lang="en">Kuhn M, Johnson K. Applied Predictive Modelling. New York: Springer; 2013.</mixed-citation></citation-alternatives></ref><ref id="cit277"><label>277</label><citation-alternatives><mixed-citation xml:lang="ru">Andersen PK, Skovgaard LT. Regression With Linear Predictors. New York: Springer; 2010.</mixed-citation><mixed-citation xml:lang="en">Andersen PK, Skovgaard LT. Regression With Linear Predictors. New York: Springer; 2010.</mixed-citation></citation-alternatives></ref><ref id="cit278"><label>278</label><citation-alternatives><mixed-citation xml:lang="ru">Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ. 2007;335(7611):136. doi: https://doi.org/10.1136/bmj.39261.471806.55</mixed-citation><mixed-citation xml:lang="en">Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ. 2007;335(7611):136. doi: https://doi.org/10.1136/bmj.39261.471806.55</mixed-citation></citation-alternatives></ref><ref id="cit279"><label>279</label><citation-alternatives><mixed-citation xml:lang="ru">Moreno L, Krishnan JA, Duran P, Ferrero F. Development and validation of a clinical prediction rule to distinguish bacterial from viral pneumonia in children. Pediatr Pulmonol. 2006;41(4): 331–337. doi: https://doi.org/10.1002/ppul.20364</mixed-citation><mixed-citation xml:lang="en">Moreno L, Krishnan JA, Duran P, Ferrero F. Development and validation of a clinical prediction rule to distinguish bacterial from viral pneumonia in children. Pediatr Pulmonol. 2006;41(4): 331–337. doi: https://doi.org/10.1002/ppul.20364</mixed-citation></citation-alternatives></ref><ref id="cit280"><label>280</label><citation-alternatives><mixed-citation xml:lang="ru">Anderson KM, Odell PM, Wilson PW, Kannel WB. Cardiovascular disease risk profiles. Am Heart J. 1991;121(1 Pt 2):293–298. doi: https://doi.org/10.1016/0002-8703(91)90861-b</mixed-citation><mixed-citation xml:lang="en">Anderson KM, Odell PM, Wilson PW, Kannel WB. Cardiovascular disease risk profiles. Am Heart J. 1991;121(1 Pt 2):293–298. doi: https://doi.org/10.1016/0002-8703(91)90861-b</mixed-citation></citation-alternatives></ref><ref id="cit281"><label>281</label><citation-alternatives><mixed-citation xml:lang="ru">Royston P, Parmar MK. Flexible parametric proportionalhazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat Med. 2002;21(15):2175–2197. doi: https://doi.org/10.1002/sim.1203</mixed-citation><mixed-citation xml:lang="en">Royston P, Parmar MK. Flexible parametric proportionalhazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat Med. 2002;21(15):2175–2197. doi: https://doi.org/10.1002/sim.1203</mixed-citation></citation-alternatives></ref><ref id="cit282"><label>282</label><citation-alternatives><mixed-citation xml:lang="ru">Hans D, Durosier C, Kanis JA, et al. Assessment of the 10-year probability of osteoporotic hip fracture combining clinical risk factors and heel bone ultrasound: the EPISEM prospective cohort of 12,958 elderly women. J Bone Miner Res. 2008;23(7):1045–1051. doi: https://doi.org/10.1359/jbmr.080229</mixed-citation><mixed-citation xml:lang="en">Hans D, Durosier C, Kanis JA, et al. Assessment of the 10-year probability of osteoporotic hip fracture combining clinical risk factors and heel bone ultrasound: the EPISEM prospective cohort of 12,958 elderly women. J Bone Miner Res. 2008;23(7):1045–1051. doi: https://doi.org/10.1359/jbmr.080229</mixed-citation></citation-alternatives></ref><ref id="cit283"><label>283</label><citation-alternatives><mixed-citation xml:lang="ru">Bohensky MA, Jolley D, Pilcher DV, et al. Prognostic models based on administrative data alone inadequately predict the survival outcomes for critically ill patients at 180 days posthospital discharge. J Crit Care. 2012;27(4):422.e11–e21. doi: https://doi.org/10.1016/j.jcrc.2012.03.008</mixed-citation><mixed-citation xml:lang="en">Bohensky MA, Jolley D, Pilcher DV, et al. Prognostic models based on administrative data alone inadequately predict the survival outcomes for critically ill patients at 180 days posthospital discharge. J Crit Care. 2012;27(4):422.e11–e21. doi: https://doi.org/10.1016/j.jcrc.2012.03.008</mixed-citation></citation-alternatives></ref><ref id="cit284"><label>284</label><citation-alternatives><mixed-citation xml:lang="ru">Barrett TW, Martin AR, Storrow AB, et al. A clinical prediction model to estimate risk for 30-day adverse events in emergency department patients with symptomatic atrial fibrillation. Ann Emerg Med. 2011;57(1):1–12. doi: https://doi.org/10.1016/j.annemergmed.2010.05.031</mixed-citation><mixed-citation xml:lang="en">Barrett TW, Martin AR, Storrow AB, et al. A clinical prediction model to estimate risk for 30-day adverse events in emergency department patients with symptomatic atrial fibrillation. Ann Emerg Med. 2011;57(1):1–12. doi: https://doi.org/10.1016/j.annemergmed.2010.05.031</mixed-citation></citation-alternatives></ref><ref id="cit285"><label>285</label><citation-alternatives><mixed-citation xml:lang="ru">Krijnen P, van Jaarsveld BC, Steyerberg EW, et al. A cli nical prediction rule for renal artery stenosis. Ann Intern Med. 1998; 129(9):705–711. doi: https://doi.org/10.7326/0003-4819-129-9-199811010-00005</mixed-citation><mixed-citation xml:lang="en">Krijnen P, van Jaarsveld BC, Steyerberg EW, et al. A cli nical prediction rule for renal artery stenosis. Ann Intern Med. 1998; 129(9):705–711. doi: https://doi.org/10.7326/0003-4819-129-9-199811010-00005</mixed-citation></citation-alternatives></ref><ref id="cit286"><label>286</label><citation-alternatives><mixed-citation xml:lang="ru">Smits M, Dippel DW, Steyerberg EW, et al. Predicting intracranial traumatic findings on computed tomography in patients with minor head injury: the CHIP prediction rule. Ann Intern Med. 2007;146(6):397–405. doi: https://doi.org/10.7326/0003-4819-146-6-200703200-00004</mixed-citation><mixed-citation xml:lang="en">Smits M, Dippel DW, Steyerberg EW, et al. Predicting intracranial traumatic findings on computed tomography in patients with minor head injury: the CHIP prediction rule. Ann Intern Med. 2007;146(6):397–405. doi: https://doi.org/10.7326/0003-4819-146-6-200703200-00004</mixed-citation></citation-alternatives></ref><ref id="cit287"><label>287</label><citation-alternatives><mixed-citation xml:lang="ru">Moons KG, Donders AR, Steyerberg EW, Harrell FE. Penalized maximum likelihood estimation to directly adjust diagnostic and prognostic prediction models for overoptimism: a clinical example. J Clin Epidemiol. 2004;57(12):1262–1270. doi: https://doi.org/10.1016/j.jclinepi.2004.01.020</mixed-citation><mixed-citation xml:lang="en">Moons KG, Donders AR, Steyerberg EW, Harrell FE. Penalized maximum likelihood estimation to directly adjust diagnostic and prognostic prediction models for overoptimism: a clinical example. J Clin Epidemiol. 2004;57(12):1262–1270. doi: https://doi.org/10.1016/j.jclinepi.2004.01.020</mixed-citation></citation-alternatives></ref><ref id="cit288"><label>288</label><citation-alternatives><mixed-citation xml:lang="ru">Mantel N. Why stepdown procedures in variable selection? Technometrics. 1970;12:621–625.</mixed-citation><mixed-citation xml:lang="en">Mantel N. Why stepdown procedures in variable selection? Technometrics. 1970;12:621–625.</mixed-citation></citation-alternatives></ref><ref id="cit289"><label>289</label><citation-alternatives><mixed-citation xml:lang="ru">Bleeker SE, Moll HA, Steyerberg EW, et al. External validation is necessary in prediction research: a clinical example. J Clin Epidemiol. 2003;56(9):826–832. doi: https://doi.org/10.1016/s0895-4356(03)00207-5</mixed-citation><mixed-citation xml:lang="en">Bleeker SE, Moll HA, Steyerberg EW, et al. External validation is necessary in prediction research: a clinical example. J Clin Epidemiol. 2003;56(9):826–832. doi: https://doi.org/10.1016/s0895-4356(03)00207-5</mixed-citation></citation-alternatives></ref><ref id="cit290"><label>290</label><citation-alternatives><mixed-citation xml:lang="ru">Steyerberg EW, Borsboom GJ, van Houwelingen HC, et al. Validation and updating of predictive logistic regression models: a study on sample size and shrinkage. Stat Med. 2004;23(16): 2567–2586. doi: https://doi.org/10.1002/sim.1844</mixed-citation><mixed-citation xml:lang="en">Steyerberg EW, Borsboom GJ, van Houwelingen HC, et al. Validation and updating of predictive logistic regression models: a study on sample size and shrinkage. Stat Med. 2004;23(16): 2567–2586. doi: https://doi.org/10.1002/sim.1844</mixed-citation></citation-alternatives></ref><ref id="cit291"><label>291</label><citation-alternatives><mixed-citation xml:lang="ru">van Houwelingen HC, Sauerbrei W. Cross-validation, shrinkage and variable selection in linear regression revisited. Open J Statist. 2013;3:79–102. doi: https://doi.org/10.4236/OJS.2013.32011</mixed-citation><mixed-citation xml:lang="en">van Houwelingen HC, Sauerbrei W. Cross-validation, shrinkage and variable selection in linear regression revisited. Open J Statist. 2013;3:79–102. doi: https://doi.org/10.4236/OJS.2013.32011</mixed-citation></citation-alternatives></ref><ref id="cit292"><label>292</label><citation-alternatives><mixed-citation xml:lang="ru">Sauerbrei W, Boulesteix AL, Binder H. Stability investigations of multivariable regression models derived from low- and highdimensional data. J Biopharm Stat. 2011;21(6):1206–1231. doi: https://doi.org/10.1080/10543406.2011.629890</mixed-citation><mixed-citation xml:lang="en">Sauerbrei W, Boulesteix AL, Binder H. Stability investigations of multivariable regression models derived from low- and highdimensional data. J Biopharm Stat. 2011;21(6):1206–1231. doi: https://doi.org/10.1080/10543406.2011.629890</mixed-citation></citation-alternatives></ref><ref id="cit293"><label>293</label><citation-alternatives><mixed-citation xml:lang="ru">Harrell FE, Lee KL, Califf RM, et al. Regression modelling strate gies for improved prognostic prediction. Stat Med. 1984;3(2): 143–152. doi: https://doi.org/10.1002/sim.4780030207</mixed-citation><mixed-citation xml:lang="en">Harrell FE, Lee KL, Califf RM, et al. Regression modelling strate gies for improved prognostic prediction. Stat Med. 1984;3(2): 143–152. doi: https://doi.org/10.1002/sim.4780030207</mixed-citation></citation-alternatives></ref><ref id="cit294"><label>294</label><citation-alternatives><mixed-citation xml:lang="ru">van Houwelingen JC, LeCessie S. Predictive value of statistical models. Stat Med. 1990;9(11):1303–1325. doi: https://doi.org/10.1002/sim.4780091109</mixed-citation><mixed-citation xml:lang="en">van Houwelingen JC, LeCessie S. Predictive value of statistical models. Stat Med. 1990;9(11):1303–1325. doi: https://doi.org/10.1002/sim.4780091109</mixed-citation></citation-alternatives></ref><ref id="cit295"><label>295</label><citation-alternatives><mixed-citation xml:lang="ru">Molinaro AM, Simon R, Pfeiffer RM. Prediction error estimation: a comparison of resampling methods. Bioinformatics. 2005;21(15): 3301–3307. doi: https://doi.org/10.1093/bioinformatics/bti499</mixed-citation><mixed-citation xml:lang="en">Molinaro AM, Simon R, Pfeiffer RM. Prediction error estimation: a comparison of resampling methods. Bioinformatics. 2005;21(15): 3301–3307. doi: https://doi.org/10.1093/bioinformatics/bti499</mixed-citation></citation-alternatives></ref><ref id="cit296"><label>296</label><citation-alternatives><mixed-citation xml:lang="ru">Chatfield C. Model uncertainty, data mining and statistical inference. J R Stat Soc A. 1995;158(3):419–466. doi: https://doi.org/10.2307/2983440</mixed-citation><mixed-citation xml:lang="en">Chatfield C. Model uncertainty, data mining and statistical inference. J R Stat Soc A. 1995;158(3):419–466. doi: https://doi.org/10.2307/2983440</mixed-citation></citation-alternatives></ref><ref id="cit297"><label>297</label><citation-alternatives><mixed-citation xml:lang="ru">Sauerbrei W, Royston P, Binder H. Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med. 2007; 26(30):5512–5528. doi: https://doi.org/10.1002/sim.3148</mixed-citation><mixed-citation xml:lang="en">Sauerbrei W, Royston P, Binder H. Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med. 2007; 26(30):5512–5528. doi: https://doi.org/10.1002/sim.3148</mixed-citation></citation-alternatives></ref><ref id="cit298"><label>298</label><citation-alternatives><mixed-citation xml:lang="ru">Heymans MW, van Buuren S, Knol DL, et al. Variable selection under multiple imputation using the bootstrap in a prognostic study. BMC Med Res Meth. 2007;7:33. doi: https://doi.org/10.1186/1471-2288-7-33</mixed-citation><mixed-citation xml:lang="en">Heymans MW, van Buuren S, Knol DL, et al. Variable selection under multiple imputation using the bootstrap in a prognostic study. BMC Med Res Meth. 2007;7:33. doi: https://doi.org/10.1186/1471-2288-7-33</mixed-citation></citation-alternatives></ref><ref id="cit299"><label>299</label><citation-alternatives><mixed-citation xml:lang="ru">Castaldi PJ, Dahabreh IJ, Ioannidis JP. An empirical assessment of validation practices for molecular classifiers. Brief Bioinform. 2011;12(3):189–202. doi: https://doi.org/10.1093/bib/bbq073</mixed-citation><mixed-citation xml:lang="en">Castaldi PJ, Dahabreh IJ, Ioannidis JP. An empirical assessment of validation practices for molecular classifiers. Brief Bioinform. 2011;12(3):189–202. doi: https://doi.org/10.1093/bib/bbq073</mixed-citation></citation-alternatives></ref><ref id="cit300"><label>300</label><citation-alternatives><mixed-citation xml:lang="ru">Varma S, Simon R. Bias in error estimation when using crossvalidation for model selection. BMC Bioinformatics. 2006;7:91. doi: https://doi.org/10.1186/1471-2105-7-91</mixed-citation><mixed-citation xml:lang="en">Varma S, Simon R. Bias in error estimation when using crossvalidation for model selection. BMC Bioinformatics. 2006;7:91. doi: https://doi.org/10.1186/1471-2105-7-91</mixed-citation></citation-alternatives></ref><ref id="cit301"><label>301</label><citation-alternatives><mixed-citation xml:lang="ru">Vach K, Sauerbrei W, Schumacher M. Variable selection and shrinkage: comparison of some approaches. Stat Neerl. 2001; 55(1):53–75. doi: https://doi.org/10.4236/OJS.2013.32011</mixed-citation><mixed-citation xml:lang="en">Vach K, Sauerbrei W, Schumacher M. Variable selection and shrinkage: comparison of some approaches. Stat Neerl. 2001; 55(1):53–75. doi: https://doi.org/10.4236/OJS.2013.32011</mixed-citation></citation-alternatives></ref><ref id="cit302"><label>302</label><citation-alternatives><mixed-citation xml:lang="ru">Lin IF, Chang WP, Liao YN. Shrinkage methods enhanced the accuracy of parameter estimation using Cox models with small number of events. J Clin Epidemiol. 2013;66(7):743–751. doi: https://doi.org/10.1016/j.jclinepi.2013.02.002</mixed-citation><mixed-citation xml:lang="en">Lin IF, Chang WP, Liao YN. Shrinkage methods enhanced the accuracy of parameter estimation using Cox models with small number of events. J Clin Epidemiol. 2013;66(7):743–751. doi: https://doi.org/10.1016/j.jclinepi.2013.02.002</mixed-citation></citation-alternatives></ref><ref id="cit303"><label>303</label><citation-alternatives><mixed-citation xml:lang="ru">Ambler G, Seaman S, Omar RZ. An evaluation of penalised survival methods for developing prognostic models with rare events. Stat Med. 2012;31(11–12):1150–1161. doi: https://doi.org/10.1002/sim.4371</mixed-citation><mixed-citation xml:lang="en">Ambler G, Seaman S, Omar RZ. An evaluation of penalised survival methods for developing prognostic models with rare events. Stat Med. 2012;31(11–12):1150–1161. doi: https://doi.org/10.1002/sim.4371</mixed-citation></citation-alternatives></ref><ref id="cit304"><label>304</label><citation-alternatives><mixed-citation xml:lang="ru">Yourman LC, Lee SJ, Schonberg MA, et al. Prognostic indices for older adults: a systematic review. JAMA. 2012;307(2):182–192. doi: https://doi.org/10.1001/jama.2011.1966</mixed-citation><mixed-citation xml:lang="en">Yourman LC, Lee SJ, Schonberg MA, et al. Prognostic indices for older adults: a systematic review. JAMA. 2012;307(2):182–192. doi: https://doi.org/10.1001/jama.2011.1966</mixed-citation></citation-alternatives></ref><ref id="cit305"><label>305</label><citation-alternatives><mixed-citation xml:lang="ru">Spelt L, Andersson B, Nilsson J, Andersson R. Prognostic models for outcome following liver resection for colorectal cancer metastases: a systematic review. Eur J Surg Oncol. 2012;38(1): 16–24. doi: https://doi.org/10.1016/j.ejso.2011.10.013</mixed-citation><mixed-citation xml:lang="en">Spelt L, Andersson B, Nilsson J, Andersson R. Prognostic models for outcome following liver resection for colorectal cancer metastases: a systematic review. Eur J Surg Oncol. 2012;38(1): 16–24. doi: https://doi.org/10.1016/j.ejso.2011.10.013</mixed-citation></citation-alternatives></ref><ref id="cit306"><label>306</label><citation-alternatives><mixed-citation xml:lang="ru">Nam RK, Kattan MW, Chin JL, et al. Prospective multiinstitutional study evaluating the performance of prostate cancer risk calculators. J Clin Oncol. 2011;29(22):2959–2964. doi: https://doi.org/10.1200/JCO.2010.32.6371</mixed-citation><mixed-citation xml:lang="en">Nam RK, Kattan MW, Chin JL, et al. Prospective multiinstitutional study evaluating the performance of prostate cancer risk calculators. J Clin Oncol. 2011;29(22):2959–2964. doi: https://doi.org/10.1200/JCO.2010.32.6371</mixed-citation></citation-alternatives></ref><ref id="cit307"><label>307</label><citation-alternatives><mixed-citation xml:lang="ru">Meffert PJ, Baumeister SE, Lerch MM, et al. Development, external validation, and comparative assessment of a new diagnostic score for hepatic steatosis. Am J Gastroenterol. 2014;109(9): 1404–1414. doi: https://doi.org/10.1038/ajg.2014.155</mixed-citation><mixed-citation xml:lang="en">Meffert PJ, Baumeister SE, Lerch MM, et al. Development, external validation, and comparative assessment of a new diagnostic score for hepatic steatosis. Am J Gastroenterol. 2014;109(9): 1404–1414. doi: https://doi.org/10.1038/ajg.2014.155</mixed-citation></citation-alternatives></ref><ref id="cit308"><label>308</label><citation-alternatives><mixed-citation xml:lang="ru">Collins GS, Altman DG. Identifying patients with undetected colorectal cancer: an independent validation of QCancer (Colorectal). Br J Cancer. 2012;107(2):260–265. doi: https://doi.org/10.1038/bjc.2012.266</mixed-citation><mixed-citation xml:lang="en">Collins GS, Altman DG. Identifying patients with undetected colorectal cancer: an independent validation of QCancer (Colorectal). Br J Cancer. 2012;107(2):260–265. doi: https://doi.org/10.1038/bjc.2012.266</mixed-citation></citation-alternatives></ref><ref id="cit309"><label>309</label><citation-alternatives><mixed-citation xml:lang="ru">Royston P, Altman DG. External validation of a Cox prognostic model: principles and methods. BMC Med Res Methodol. 2013;13:33. doi: https://doi.org/10.1186/1471-2288-13-33</mixed-citation><mixed-citation xml:lang="en">Royston P, Altman DG. External validation of a Cox prognostic model: principles and methods. BMC Med Res Methodol. 2013;13:33. doi: https://doi.org/10.1186/1471-2288-13-33</mixed-citation></citation-alternatives></ref><ref id="cit310"><label>310</label><citation-alternatives><mixed-citation xml:lang="ru">Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008;26(8):1364–1370. doi: https://doi.org/10.1200/JCO.2007.12.9791</mixed-citation><mixed-citation xml:lang="en">Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008;26(8):1364–1370. doi: https://doi.org/10.1200/JCO.2007.12.9791</mixed-citation></citation-alternatives></ref><ref id="cit311"><label>311</label><citation-alternatives><mixed-citation xml:lang="ru">Zivanovic O, Jacks LM, Iasonos A, et al. A nomogram to predict postresection 5-year overall survival for patients with uterine leiomyosarcoma. Cancer. 2012;118(3):660–669. doi: https://doi.org/10.1002/cncr.26333</mixed-citation><mixed-citation xml:lang="en">Zivanovic O, Jacks LM, Iasonos A, et al. A nomogram to predict postresection 5-year overall survival for patients with uterine leiomyosarcoma. Cancer. 2012;118(3):660–669. doi: https://doi.org/10.1002/cncr.26333</mixed-citation></citation-alternatives></ref><ref id="cit312"><label>312</label><citation-alternatives><mixed-citation xml:lang="ru">Kanis JA, Oden A, Johnell O, et al. The use of clinical risk factors enhances the performance of BMD in the prediction of hip and osteoporotic fractures in men and women. Osteoporos Int. 2007;18(8):1033–1046. doi: https://doi.org/10.1007/s00198-007-0343-y</mixed-citation><mixed-citation xml:lang="en">Kanis JA, Oden A, Johnell O, et al. The use of clinical risk factors enhances the performance of BMD in the prediction of hip and osteoporotic fractures in men and women. Osteoporos Int. 2007;18(8):1033–1046. doi: https://doi.org/10.1007/s00198-007-0343-y</mixed-citation></citation-alternatives></ref><ref id="cit313"><label>313</label><citation-alternatives><mixed-citation xml:lang="ru">Papaioannou A, Morin S, Cheung AM, et al. 2010 clinical practice guidelines for the diagnosis and management of osteoporosis in Canada: summary. CMAJ. 2010;182(17):1864–1873. doi: https://doi.org/10.1503/cmaj.100771</mixed-citation><mixed-citation xml:lang="en">Papaioannou A, Morin S, Cheung AM, et al. 2010 clinical practice guidelines for the diagnosis and management of osteoporosis in Canada: summary. CMAJ. 2010;182(17):1864–1873. doi: https://doi.org/10.1503/cmaj.100771</mixed-citation></citation-alternatives></ref><ref id="cit314"><label>314</label><citation-alternatives><mixed-citation xml:lang="ru">Collins GS, Michaëlsson K. Fracture risk assessment: state of the art, methodologically unsound, or poorly reported? Curr Osteoporos Rep. 2012;10(3):199–207. doi: https://doi.org/10.1007/s11914-012-0108-1</mixed-citation><mixed-citation xml:lang="en">Collins GS, Michaëlsson K. Fracture risk assessment: state of the art, methodologically unsound, or poorly reported? Curr Osteoporos Rep. 2012;10(3):199–207. doi: https://doi.org/10.1007/s11914-012-0108-1</mixed-citation></citation-alternatives></ref><ref id="cit315"><label>315</label><citation-alternatives><mixed-citation xml:lang="ru">Collins GS, Mallett S, Altman DG. Predicting risk of osteoporotic and hip fracture in the United Kingdom: prospective independent and external validation of QFractureScores. BMJ. 2011;342:d3651. doi: https://doi.org/10.1136/bmj.d3651</mixed-citation><mixed-citation xml:lang="en">Collins GS, Mallett S, Altman DG. Predicting risk of osteoporotic and hip fracture in the United Kingdom: prospective independent and external validation of QFractureScores. BMJ. 2011;342:d3651. doi: https://doi.org/10.1136/bmj.d3651</mixed-citation></citation-alternatives></ref><ref id="cit316"><label>316</label><citation-alternatives><mixed-citation xml:lang="ru">Järvinen TL, Jokihaara J, Guy P, et al. Conflicts at the heart of the FRAX tool. CMAJ. 2014;186(3):165–167. doi: https://doi.org/10.1503/cmaj.121874</mixed-citation><mixed-citation xml:lang="en">Järvinen TL, Jokihaara J, Guy P, et al. Conflicts at the heart of the FRAX tool. CMAJ. 2014;186(3):165–167. doi: https://doi.org/10.1503/cmaj.121874</mixed-citation></citation-alternatives></ref><ref id="cit317"><label>317</label><citation-alternatives><mixed-citation xml:lang="ru">Balmaña J, Stockwell DH, Steyerberg EW, et al. Prediction of MLH1 and MSH2 mutations in Lynch syndrome. JAMA. 2006;296(12):1469–1478. doi: https://doi.org/10.1001/jama.296.12.1469</mixed-citation><mixed-citation xml:lang="en">Balmaña J, Stockwell DH, Steyerberg EW, et al. Prediction of MLH1 and MSH2 mutations in Lynch syndrome. JAMA. 2006;296(12):1469–1478. doi: https://doi.org/10.1001/jama.296.12.1469</mixed-citation></citation-alternatives></ref><ref id="cit318"><label>318</label><citation-alternatives><mixed-citation xml:lang="ru">Bruins Slot MH, Rutten FH, van der Heijden GJ, et al. Diagnosing acute coronary syndrome in primary care: comparison of the physicians’ risk estimation and a clinical decision rule. Fam Pract. 2011;28(3):323–328. doi: https://doi.org/10.1093/fampra/cmq116</mixed-citation><mixed-citation xml:lang="en">Bruins Slot MH, Rutten FH, van der Heijden GJ, et al. Diagnosing acute coronary syndrome in primary care: comparison of the physicians’ risk estimation and a clinical decision rule. Fam Pract. 2011;28(3):323–328. doi: https://doi.org/10.1093/fampra/cmq116</mixed-citation></citation-alternatives></ref><ref id="cit319"><label>319</label><citation-alternatives><mixed-citation xml:lang="ru">Suarthana E, Vergouwe Y, Moons KG, et al. A diagnostic model for the detection of sensitization to wheat allergens was developed and validated in bakery workers. J Clin Epidemiol. 2010;63(9): 1011–1019. doi: https://doi.org/10.1016/j.jclinepi.2009.10.008</mixed-citation><mixed-citation xml:lang="en">Suarthana E, Vergouwe Y, Moons KG, et al. A diagnostic model for the detection of sensitization to wheat allergens was developed and validated in bakery workers. J Clin Epidemiol. 2010;63(9): 1011–1019. doi: https://doi.org/10.1016/j.jclinepi.2009.10.008</mixed-citation></citation-alternatives></ref><ref id="cit320"><label>320</label><citation-alternatives><mixed-citation xml:lang="ru">Uno H, Cai T, Pencina MJ, et al. On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat Med. 2011;30(10):1105–1117. doi: https://doi.org/10.1002/sim.4154</mixed-citation><mixed-citation xml:lang="en">Uno H, Cai T, Pencina MJ, et al. On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat Med. 2011;30(10):1105–1117. doi: https://doi.org/10.1002/sim.4154</mixed-citation></citation-alternatives></ref><ref id="cit321"><label>321</label><citation-alternatives><mixed-citation xml:lang="ru">Akazawa K. Measures of explained variation for a regression model used in survival analysis. J Med Syst. 1997;21(4):229–238. doi: https://doi.org/10.1023/a:1022884504683</mixed-citation><mixed-citation xml:lang="en">Akazawa K. Measures of explained variation for a regression model used in survival analysis. J Med Syst. 1997;21(4):229–238. doi: https://doi.org/10.1023/a:1022884504683</mixed-citation></citation-alternatives></ref><ref id="cit322"><label>322</label><citation-alternatives><mixed-citation xml:lang="ru">Choodari-Oskooei B, Royston P, Parmar MK. A simulation study of predictive ability measures in a survival model I: explained variation measures. Stat Med. 2012;31(23):2627–2643. doi: https://doi.org/10.1002/sim.4242</mixed-citation><mixed-citation xml:lang="en">Choodari-Oskooei B, Royston P, Parmar MK. A simulation study of predictive ability measures in a survival model I: explained variation measures. Stat Med. 2012;31(23):2627–2643. doi: https://doi.org/10.1002/sim.4242</mixed-citation></citation-alternatives></ref><ref id="cit323"><label>323</label><citation-alternatives><mixed-citation xml:lang="ru">Heller G. A measure of explained risk in the proportional ha zards model. Biostatistics. 2012;13(2):315–325. doi: https://doi.org/10.1093/biostatistics/kxr047</mixed-citation><mixed-citation xml:lang="en">Heller G. A measure of explained risk in the proportional ha zards model. Biostatistics. 2012;13(2):315–325. doi: https://doi.org/10.1093/biostatistics/kxr047</mixed-citation></citation-alternatives></ref><ref id="cit324"><label>324</label><citation-alternatives><mixed-citation xml:lang="ru">Korn EL, Simon R. Measures of explained variation for survival data. Stat Med. 1990;9(5):487–503. doi: https://doi.org/10.1002/sim.4780090503</mixed-citation><mixed-citation xml:lang="en">Korn EL, Simon R. Measures of explained variation for survival data. Stat Med. 1990;9(5):487–503. doi: https://doi.org/10.1002/sim.4780090503</mixed-citation></citation-alternatives></ref><ref id="cit325"><label>325</label><citation-alternatives><mixed-citation xml:lang="ru">Mittlböck M, Schemper M. Explained variation for logistic regression. Stat Med. 1996;15(19):1987–1997. doi: https://doi.org/10.1002/(SICI)1097-0258(19961015)15:193.0.CO;2-9</mixed-citation><mixed-citation xml:lang="en">Mittlböck M, Schemper M. Explained variation for logistic regression. Stat Med. 1996;15(19):1987–1997. doi: https://doi.org/10.1002/(SICI)1097-0258(19961015)15:193.0.CO;2-9</mixed-citation></citation-alternatives></ref><ref id="cit326"><label>326</label><citation-alternatives><mixed-citation xml:lang="ru">Royston P. Explained variation for survival models. Stata Journal. 2006;6(1):83–96. doi: https://doi.org/10.1177/1536867X0600600105</mixed-citation><mixed-citation xml:lang="en">Royston P. Explained variation for survival models. Stata Journal. 2006;6(1):83–96. doi: https://doi.org/10.1177/1536867X0600600105</mixed-citation></citation-alternatives></ref><ref id="cit327"><label>327</label><citation-alternatives><mixed-citation xml:lang="ru">Schemper M. Predictive accuracy and explained variation. Stat Med. 2003;22(14):2299–2308. doi: https://doi.org/10.1002/sim.1486</mixed-citation><mixed-citation xml:lang="en">Schemper M. Predictive accuracy and explained variation. Stat Med. 2003;22(14):2299–2308. doi: https://doi.org/10.1002/sim.1486</mixed-citation></citation-alternatives></ref><ref id="cit328"><label>328</label><citation-alternatives><mixed-citation xml:lang="ru">Schemper M, Henderson R. Predictive accuracy and explained variation in Cox regression. Biometrics. 2000;56(1):249–255. doi: https://doi.org/10.1111/j.0006-341x.2000.00249.x</mixed-citation><mixed-citation xml:lang="en">Schemper M, Henderson R. Predictive accuracy and explained variation in Cox regression. Biometrics. 2000;56(1):249–255. doi: https://doi.org/10.1111/j.0006-341x.2000.00249.x</mixed-citation></citation-alternatives></ref><ref id="cit329"><label>329</label><citation-alternatives><mixed-citation xml:lang="ru">Schemper M, Stare J. Explained variation in survival analysis. Stat Med. 1996;15(19):1999–2012. doi: https://doi.org/10.1002/(SICI)1097-0258(19961015)15:193.0.CO;2-D</mixed-citation><mixed-citation xml:lang="en">Schemper M, Stare J. Explained variation in survival analysis. Stat Med. 1996;15(19):1999–2012. doi: https://doi.org/10.1002/(SICI)1097-0258(19961015)15:193.0.CO;2-D</mixed-citation></citation-alternatives></ref><ref id="cit330"><label>330</label><citation-alternatives><mixed-citation xml:lang="ru">Gerds T, Schumacher M. Consistent estimation of the expected Brier score in general survival models with right-censored event times. Biom J. 2006;48(6):1029–1040. doi: https://doi.org/10.1002/bimj.200610301</mixed-citation><mixed-citation xml:lang="en">Gerds T, Schumacher M. Consistent estimation of the expected Brier score in general survival models with right-censored event times. Biom J. 2006;48(6):1029–1040. doi: https://doi.org/10.1002/bimj.200610301</mixed-citation></citation-alternatives></ref><ref id="cit331"><label>331</label><citation-alternatives><mixed-citation xml:lang="ru">Rufibach K. Use of Brier score to assess binary predictions. J Clin Epidemiol. 2010;63(8):938–939. doi: https://doi.org/10.1016/j.jclinepi.2009.11.009</mixed-citation><mixed-citation xml:lang="en">Rufibach K. Use of Brier score to assess binary predictions. J Clin Epidemiol. 2010;63(8):938–939. doi: https://doi.org/10.1016/j.jclinepi.2009.11.009</mixed-citation></citation-alternatives></ref><ref id="cit332"><label>332</label><citation-alternatives><mixed-citation xml:lang="ru">Gerds TA, Cai T, Schumacher M. The performance of risk prediction models. Biom J. 2008;50(4):457–479. doi: https://doi.org/10.1002/bimj.200810443</mixed-citation><mixed-citation xml:lang="en">Gerds TA, Cai T, Schumacher M. The performance of risk prediction models. Biom J. 2008;50(4):457–479. doi: https://doi.org/10.1002/bimj.200810443</mixed-citation></citation-alternatives></ref><ref id="cit333"><label>333</label><citation-alternatives><mixed-citation xml:lang="ru">Royston P, Sauerbrei W. A new measure of prognostic separation in survival data. Stat Med. 2004;23(5):723–748. doi: https://doi.org/10.1002/sim.1621</mixed-citation><mixed-citation xml:lang="en">Royston P, Sauerbrei W. A new measure of prognostic separation in survival data. Stat Med. 2004;23(5):723–748. doi: https://doi.org/10.1002/sim.1621</mixed-citation></citation-alternatives></ref><ref id="cit334"><label>334</label><citation-alternatives><mixed-citation xml:lang="ru">DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–845.</mixed-citation><mixed-citation xml:lang="en">DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–845.</mixed-citation></citation-alternatives></ref><ref id="cit335"><label>335</label><citation-alternatives><mixed-citation xml:lang="ru">Demler OV, Pencina MJ, D’Agostino RB. Misuse of DeLong test to compare AUCs for nested models. Stat Med. 2012;31(23): 2577–2587. doi: https://doi.org/10.1002/sim.5328</mixed-citation><mixed-citation xml:lang="en">Demler OV, Pencina MJ, D’Agostino RB. Misuse of DeLong test to compare AUCs for nested models. Stat Med. 2012;31(23): 2577–2587. doi: https://doi.org/10.1002/sim.5328</mixed-citation></citation-alternatives></ref><ref id="cit336"><label>336</label><citation-alternatives><mixed-citation xml:lang="ru">Moonesinghe SR, Mythen MG, Das P, et al. Risk stratification tools for predicting morbidity and mortality in adult patients undergoing major surgery: qualitative systematic review. Anesthesiology. 2013;119(4):959–981. doi: https://doi.org/10.1097/ALN.0b013e3182a4e94d</mixed-citation><mixed-citation xml:lang="en">Moonesinghe SR, Mythen MG, Das P, et al. Risk stratification tools for predicting morbidity and mortality in adult patients undergoing major surgery: qualitative systematic review. Anesthesiology. 2013;119(4):959–981. doi: https://doi.org/10.1097/ALN.0b013e3182a4e94d</mixed-citation></citation-alternatives></ref><ref id="cit337"><label>337</label><citation-alternatives><mixed-citation xml:lang="ru">Wallace E, Stuart E, Vaughan N, et al. Risk prediction models to predict emergency hospital admission in community-dwelling adults: a systematic review. Med Care. 2014;52(8):751–765. doi: https://doi.org/10.1097/MLR.0000000000000171</mixed-citation><mixed-citation xml:lang="en">Wallace E, Stuart E, Vaughan N, et al. Risk prediction models to predict emergency hospital admission in community-dwelling adults: a systematic review. Med Care. 2014;52(8):751–765. doi: https://doi.org/10.1097/MLR.0000000000000171</mixed-citation></citation-alternatives></ref><ref id="cit338"><label>338</label><citation-alternatives><mixed-citation xml:lang="ru">Widera C, Pencina MJ, Bobadilla M, et al. Incremental prognostic value of biomarkers beyond the GRACE (Global Registry of Acute Coronary Events) score and high-sensitivity cardiac troponin T in non-ST-elevation acute coronary syndrome. Clin Chem. 2013;59(10):1497–1505. doi: https://doi.org/10.1373/clinchem.2013.206185</mixed-citation><mixed-citation xml:lang="en">Widera C, Pencina MJ, Bobadilla M, et al. Incremental prognostic value of biomarkers beyond the GRACE (Global Registry of Acute Coronary Events) score and high-sensitivity cardiac troponin T in non-ST-elevation acute coronary syndrome. Clin Chem. 2013;59(10):1497–1505. doi: https://doi.org/10.1373/clinchem.2013.206185</mixed-citation></citation-alternatives></ref><ref id="cit339"><label>339</label><citation-alternatives><mixed-citation xml:lang="ru">Pencina MJ, D’Agostino RB, D’Agostino RB, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157–172. doi: https://doi.org/10.1002/sim.2929</mixed-citation><mixed-citation xml:lang="en">Pencina MJ, D’Agostino RB, D’Agostino RB, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157–172. doi: https://doi.org/10.1002/sim.2929</mixed-citation></citation-alternatives></ref><ref id="cit340"><label>340</label><citation-alternatives><mixed-citation xml:lang="ru">Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation. 2007;115(7):928–935. doi: https://doi.org/10.1161/CIRCULATIONAHA.106.672402</mixed-citation><mixed-citation xml:lang="en">Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation. 2007;115(7):928–935. doi: https://doi.org/10.1161/CIRCULATIONAHA.106.672402</mixed-citation></citation-alternatives></ref><ref id="cit341"><label>341</label><citation-alternatives><mixed-citation xml:lang="ru">Hlatky MA, Greenland P, Arnett DK, et al. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation. 2009;119(17):2408–2416. doi: https://doi.org/10.1161/CIRCULATIONAHA.109.192278</mixed-citation><mixed-citation xml:lang="en">Hlatky MA, Greenland P, Arnett DK, et al. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation. 2009;119(17):2408–2416. doi: https://doi.org/10.1161/CIRCULATIONAHA.109.192278</mixed-citation></citation-alternatives></ref><ref id="cit342"><label>342</label><citation-alternatives><mixed-citation xml:lang="ru">Cook NR. Assessing the incremental role of novel and emerging risk factors. Curr Cardiovasc Risk Rep. 2010;4(2):112–119. doi: https://doi.org/10.1007/s12170-010-0084-x</mixed-citation><mixed-citation xml:lang="en">Cook NR. Assessing the incremental role of novel and emerging risk factors. Curr Cardiovasc Risk Rep. 2010;4(2):112–119. doi: https://doi.org/10.1007/s12170-010-0084-x</mixed-citation></citation-alternatives></ref><ref id="cit343"><label>343</label><citation-alternatives><mixed-citation xml:lang="ru">Vickers AJ, Cronin AM, Begg CB. One statistical test is sufficient for assessing new predictive markers. BMC Med Res Methodol. 2011;11:13. doi: https://doi.org/10.1186/1471-2288-11-13</mixed-citation><mixed-citation xml:lang="en">Vickers AJ, Cronin AM, Begg CB. One statistical test is sufficient for assessing new predictive markers. BMC Med Res Methodol. 2011;11:13. doi: https://doi.org/10.1186/1471-2288-11-13</mixed-citation></citation-alternatives></ref><ref id="cit344"><label>344</label><citation-alternatives><mixed-citation xml:lang="ru">Cook NR, Ridker PM. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med. 2009;150(11):795–802. doi: https://doi.org/10.7326/0003-4819-150-11-200906020-00007</mixed-citation><mixed-citation xml:lang="en">Cook NR, Ridker PM. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med. 2009;150(11):795–802. doi: https://doi.org/10.7326/0003-4819-150-11-200906020-00007</mixed-citation></citation-alternatives></ref><ref id="cit345"><label>345</label><citation-alternatives><mixed-citation xml:lang="ru">Cook NR, Paynter NP. Performance of reclassification statistics in comparing risk prediction models. Biom J. 2011;53(2):237–258. doi: https://doi.org/10.1002/bimj.201000078</mixed-citation><mixed-citation xml:lang="en">Cook NR, Paynter NP. Performance of reclassification statistics in comparing risk prediction models. Biom J. 2011;53(2):237–258. doi: https://doi.org/10.1002/bimj.201000078</mixed-citation></citation-alternatives></ref><ref id="cit346"><label>346</label><citation-alternatives><mixed-citation xml:lang="ru">Cook NR. Clinically relevant measures of fit? A note of caution. Am J Epidemiol. 2012;176(6):488–491. doi: https://doi.org/10.1093/aje/kws208</mixed-citation><mixed-citation xml:lang="en">Cook NR. Clinically relevant measures of fit? A note of caution. Am J Epidemiol. 2012;176(6):488–491. doi: https://doi.org/10.1093/aje/kws208</mixed-citation></citation-alternatives></ref><ref id="cit347"><label>347</label><citation-alternatives><mixed-citation xml:lang="ru">Pencina MJ, D’Agostino RB, Pencina KM, et al. Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol. 2012;176(6):473–481. doi: https://doi.org/10.1093/aje/kws207</mixed-citation><mixed-citation xml:lang="en">Pencina MJ, D’Agostino RB, Pencina KM, et al. Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol. 2012;176(6):473–481. doi: https://doi.org/10.1093/aje/kws207</mixed-citation></citation-alternatives></ref><ref id="cit348"><label>348</label><citation-alternatives><mixed-citation xml:lang="ru">Pencina MJ, D’Agostino RB, Vasan RS. Statistical methods for assessment of added usefulness of new biomarkers. Clin Chem Lab Med. 2010;48(12):1703–1711. doi: https://doi.org/10.1515/CCLM.2010.340</mixed-citation><mixed-citation xml:lang="en">Pencina MJ, D’Agostino RB, Vasan RS. Statistical methods for assessment of added usefulness of new biomarkers. Clin Chem Lab Med. 2010;48(12):1703–1711. doi: https://doi.org/10.1515/CCLM.2010.340</mixed-citation></citation-alternatives></ref><ref id="cit349"><label>349</label><citation-alternatives><mixed-citation xml:lang="ru">Van Calster B, Vickers AJ, Pencina MJ, et al. Evaluation of markers and risk prediction models: overview of relationships between NRI and decision-analytic measures. Med Decis Making. 2013;33(4): 490–501. doi: https://doi.org/10.1177/0272989X12470757</mixed-citation><mixed-citation xml:lang="en">Van Calster B, Vickers AJ, Pencina MJ, et al. Evaluation of markers and risk prediction models: overview of relationships between NRI and decision-analytic measures. Med Decis Making. 2013;33(4): 490–501. doi: https://doi.org/10.1177/0272989X12470757</mixed-citation></citation-alternatives></ref><ref id="cit350"><label>350</label><citation-alternatives><mixed-citation xml:lang="ru">Hilden J, Gerds TA. A note on the evaluation of novel biomarkers: do not rely on integrated discrimination improvement and net reclassification index. Stat Med. 2014;33(19):3405–3414. doi: https://doi.org/10.1002/sim.5804</mixed-citation><mixed-citation xml:lang="en">Hilden J, Gerds TA. A note on the evaluation of novel biomarkers: do not rely on integrated discrimination improvement and net reclassification index. Stat Med. 2014;33(19):3405–3414. doi: https://doi.org/10.1002/sim.5804</mixed-citation></citation-alternatives></ref><ref id="cit351"><label>351</label><citation-alternatives><mixed-citation xml:lang="ru">Pepe MS. Problems with risk reclassification methods for evaluating prediction models. Am J Epidemiol. 2011;173(11): 1327–1335. doi: https://doi.org/10.1093/aje/kwr013</mixed-citation><mixed-citation xml:lang="en">Pepe MS. Problems with risk reclassification methods for evaluating prediction models. Am J Epidemiol. 2011;173(11): 1327–1335. doi: https://doi.org/10.1093/aje/kwr013</mixed-citation></citation-alternatives></ref><ref id="cit352"><label>352</label><citation-alternatives><mixed-citation xml:lang="ru">Mihaescu R, van Zitteren M, van Hoek M, et al. Improvement of risk prediction by genomic profiling: reclassification measures versus the area under the receiver operating characteristic curve. Am J Epidemiol. 2010;172(3):353–361. doi: https://doi.org/10.1093/aje/kwq122</mixed-citation><mixed-citation xml:lang="en">Mihaescu R, van Zitteren M, van Hoek M, et al. Improvement of risk prediction by genomic profiling: reclassification measures versus the area under the receiver operating characteristic curve. Am J Epidemiol. 2010;172(3):353–361. doi: https://doi.org/10.1093/aje/kwq122</mixed-citation></citation-alternatives></ref><ref id="cit353"><label>353</label><citation-alternatives><mixed-citation xml:lang="ru">Mühlenbruch K, Heraclides A, Steyerberg EW, et al. Assessing improvement in disease prediction using net reclassification improvement: impact of risk cut-offs and number of risk categories. Eur J Epidemiol. 2013;28(1):25–33. doi: https://doi.org/10.1007/s10654-012-9744-0</mixed-citation><mixed-citation xml:lang="en">Mühlenbruch K, Heraclides A, Steyerberg EW, et al. Assessing improvement in disease prediction using net reclassification improvement: impact of risk cut-offs and number of risk categories. Eur J Epidemiol. 2013;28(1):25–33. doi: https://doi.org/10.1007/s10654-012-9744-0</mixed-citation></citation-alternatives></ref><ref id="cit354"><label>354</label><citation-alternatives><mixed-citation xml:lang="ru">Pepe M, Fang J, Feng Z, et al. The Net Reclassification Index (NRI): a Misleading Measure of Prediction Improvement with Miscalibrated or Overfit Models. UW Biostatistics Working Paper Series. Working Paper 392. Madison, WI: University of Wisconsin; 2013.</mixed-citation><mixed-citation xml:lang="en">Pepe M, Fang J, Feng Z, et al. The Net Reclassification Index (NRI): a Misleading Measure of Prediction Improvement with Miscalibrated or Overfit Models. UW Biostatistics Working Paper Series. Working Paper 392. Madison, WI: University of Wisconsin; 2013.</mixed-citation></citation-alternatives></ref><ref id="cit355"><label>355</label><citation-alternatives><mixed-citation xml:lang="ru">Vickers AJ, Pepe M. Does the net reclassification improvement help us evaluate models and markers? Ann Intern Med. 2014;160(2):136–137. doi: https://doi.org/10.7326/M13-2841</mixed-citation><mixed-citation xml:lang="en">Vickers AJ, Pepe M. Does the net reclassification improvement help us evaluate models and markers? Ann Intern Med. 2014;160(2):136–137. doi: https://doi.org/10.7326/M13-2841</mixed-citation></citation-alternatives></ref><ref id="cit356"><label>356</label><citation-alternatives><mixed-citation xml:lang="ru">Hilden J. Commentary: On NRI, IDI, and “good-looking” statistics with nothing underneath. Epidemiology. 2014;25(2): 265–267. doi: https://doi.org/10.1097/EDE.0000000000000063</mixed-citation><mixed-citation xml:lang="en">Hilden J. Commentary: On NRI, IDI, and “good-looking” statistics with nothing underneath. Epidemiology. 2014;25(2): 265–267. doi: https://doi.org/10.1097/EDE.0000000000000063</mixed-citation></citation-alternatives></ref><ref id="cit357"><label>357</label><citation-alternatives><mixed-citation xml:lang="ru">Leening MJ, Vedder MM, Witteman JCM, et al. Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician’s guide. Ann Intern Med. 2014;160(2):122–131. doi: https://doi.org/10.7326/M13-1522</mixed-citation><mixed-citation xml:lang="en">Leening MJ, Vedder MM, Witteman JCM, et al. Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician’s guide. Ann Intern Med. 2014;160(2):122–131. doi: https://doi.org/10.7326/M13-1522</mixed-citation></citation-alternatives></ref><ref id="cit358"><label>358</label><citation-alternatives><mixed-citation xml:lang="ru">Al-Radi OO, Harrell FE, Caldarone CA, et al. Case complexity scores in congenital heart surgery: a comparative study of the Aristotle Basic Complexity score and the Risk Adjustment in Congenital Heart Surgery (RACHS-1) system. J Thorac Cardiovasc Surg. 2007;133(4):865–875. doi: https://doi.org/10.1016/j.jtcvs.2006.05.071</mixed-citation><mixed-citation xml:lang="en">Al-Radi OO, Harrell FE, Caldarone CA, et al. Case complexity scores in congenital heart surgery: a comparative study of the Aristotle Basic Complexity score and the Risk Adjustment in Congenital Heart Surgery (RACHS-1) system. J Thorac Cardiovasc Surg. 2007;133(4):865–875. doi: https://doi.org/10.1016/j.jtcvs.2006.05.071</mixed-citation></citation-alternatives></ref><ref id="cit359"><label>359</label><citation-alternatives><mixed-citation xml:lang="ru">Localio AR, Goodman S. Beyond the usual prediction accuracy metrics: reporting results for clinical decision making. Ann Intern Med. 2012;157(4):294–295. doi: https://doi.org/10.7326/0003-4819-157-4-201208210-00014</mixed-citation><mixed-citation xml:lang="en">Localio AR, Goodman S. Beyond the usual prediction accuracy metrics: reporting results for clinical decision making. Ann Intern Med. 2012;157(4):294–295. doi: https://doi.org/10.7326/0003-4819-157-4-201208210-00014</mixed-citation></citation-alternatives></ref><ref id="cit360"><label>360</label><citation-alternatives><mixed-citation xml:lang="ru">Van Calster B, Vickers AJ. Calibration of risk prediction models: impact on decision-analytic performance. Med Decis Making. 2015;35(2):162–169. doi: https://doi.org/10.1177/0272989X14547233</mixed-citation><mixed-citation xml:lang="en">Van Calster B, Vickers AJ. Calibration of risk prediction models: impact on decision-analytic performance. Med Decis Making. 2015;35(2):162–169. doi: https://doi.org/10.1177/0272989X14547233</mixed-citation></citation-alternatives></ref><ref id="cit361"><label>361</label><citation-alternatives><mixed-citation xml:lang="ru">Vickers AJ. Decision analysis for the evaluation of diagnostic tests, prediction models and molecular markers. Am Stat. 2008;62(4): 314–320. doi: https://doi.org/10.1198/000313008X370302</mixed-citation><mixed-citation xml:lang="en">Vickers AJ. Decision analysis for the evaluation of diagnostic tests, prediction models and molecular markers. Am Stat. 2008;62(4): 314–320. doi: https://doi.org/10.1198/000313008X370302</mixed-citation></citation-alternatives></ref><ref id="cit362"><label>362</label><citation-alternatives><mixed-citation xml:lang="ru">Vickers AJ, Cronin AM, Kattan MW, et al. Clinical benefits of a multivariate prediction model for bladder cancer: a decision analytic approach. Cancer. 2009;115(23):5460–5469. doi: https://doi.org/10.1002/cncr.24615</mixed-citation><mixed-citation xml:lang="en">Vickers AJ, Cronin AM, Kattan MW, et al. Clinical benefits of a multivariate prediction model for bladder cancer: a decision analytic approach. Cancer. 2009;115(23):5460–5469. doi: https://doi.org/10.1002/cncr.24615</mixed-citation></citation-alternatives></ref><ref id="cit363"><label>363</label><citation-alternatives><mixed-citation xml:lang="ru">Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26(6): 565–574. doi: https://doi.org/10.1177/0272989X06295361</mixed-citation><mixed-citation xml:lang="en">Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26(6): 565–574. doi: https://doi.org/10.1177/0272989X06295361</mixed-citation></citation-alternatives></ref><ref id="cit364"><label>364</label><citation-alternatives><mixed-citation xml:lang="ru">Baker SG. Putting risk prediction in perspective: relative utility curves. J Natl Cancer Inst. 2009;101(22):1538–1542. doi: https://doi.org/10.1093/jnci/djp353</mixed-citation><mixed-citation xml:lang="en">Baker SG. Putting risk prediction in perspective: relative utility curves. J Natl Cancer Inst. 2009;101(22):1538–1542. doi: https://doi.org/10.1093/jnci/djp353</mixed-citation></citation-alternatives></ref><ref id="cit365"><label>365</label><citation-alternatives><mixed-citation xml:lang="ru">Baker SG, Cook NR, Vickers A, Kramer BS. Using relative utility curves to evaluate risk prediction. J R Stat Soc Ser A Stat Soc. 2009;172(4):729–748. doi: https://doi.org/10.1111/j.1467-985X.2009.00592.x</mixed-citation><mixed-citation xml:lang="en">Baker SG, Cook NR, Vickers A, Kramer BS. Using relative utility curves to evaluate risk prediction. J R Stat Soc Ser A Stat Soc. 2009;172(4):729–748. doi: https://doi.org/10.1111/j.1467-985X.2009.00592.x</mixed-citation></citation-alternatives></ref><ref id="cit366"><label>366</label><citation-alternatives><mixed-citation xml:lang="ru">Baker SG, Kramer BS. Evaluating a new marker for risk prediction: decision analysis to the rescue. Discov Med. 2012; 14(76):181–188.</mixed-citation><mixed-citation xml:lang="en">Baker SG, Kramer BS. Evaluating a new marker for risk prediction: decision analysis to the rescue. Discov Med. 2012; 14(76):181–188.</mixed-citation></citation-alternatives></ref><ref id="cit367"><label>367</label><citation-alternatives><mixed-citation xml:lang="ru">Moons KG, de Groot JA, Linnet K, et al. Quantifying the added value of a diagnostic test or marker. Clin Chem. 2012;58(10): 1408–1417. doi: https://doi.org/10.1373/clinchem.2012.182550</mixed-citation><mixed-citation xml:lang="en">Moons KG, de Groot JA, Linnet K, et al. Quantifying the added value of a diagnostic test or marker. Clin Chem. 2012;58(10): 1408–1417. doi: https://doi.org/10.1373/clinchem.2012.182550</mixed-citation></citation-alternatives></ref><ref id="cit368"><label>368</label><citation-alternatives><mixed-citation xml:lang="ru">Held U, Bové DS, Steurer J, Held L. Validating and updating a risk model for pneumonia — a case study. BMC Med Res Methodol. 2012;12:99. doi: https://doi.org/10.1186/1471-2288-12-99</mixed-citation><mixed-citation xml:lang="en">Held U, Bové DS, Steurer J, Held L. Validating and updating a risk model for pneumonia — a case study. BMC Med Res Methodol. 2012;12:99. doi: https://doi.org/10.1186/1471-2288-12-99</mixed-citation></citation-alternatives></ref><ref id="cit369"><label>369</label><citation-alternatives><mixed-citation xml:lang="ru">Cindolo L, Chiodini P, Gallo C, et al. Validation by calibration of the UCLA integrated staging system prognostic model for nonmetastatic renal cell carcinoma after nephrectomy. Cancer. 2008;113(1):65–71. doi: https://doi.org/10.1002/cncr.23517</mixed-citation><mixed-citation xml:lang="en">Cindolo L, Chiodini P, Gallo C, et al. Validation by calibration of the UCLA integrated staging system prognostic model for nonmetastatic renal cell carcinoma after nephrectomy. Cancer. 2008;113(1):65–71. doi: https://doi.org/10.1002/cncr.23517</mixed-citation></citation-alternatives></ref><ref id="cit370"><label>370</label><citation-alternatives><mixed-citation xml:lang="ru">Baart AM, Atsma F, McSweeney EN, et al. External validation and updating of a Dutch prediction model for low hemoglobin deferral in Irish whole blood donors. Transfusion. 2014;54(3 Pt 2): 762–769. doi: https://doi.org/10.1111/trf.12211</mixed-citation><mixed-citation xml:lang="en">Baart AM, Atsma F, McSweeney EN, et al. External validation and updating of a Dutch prediction model for low hemoglobin deferral in Irish whole blood donors. Transfusion. 2014;54(3 Pt 2): 762–769. doi: https://doi.org/10.1111/trf.12211</mixed-citation></citation-alternatives></ref><ref id="cit371"><label>371</label><citation-alternatives><mixed-citation xml:lang="ru">Chalmers I, Glasziou P. Avoidable waste in the production and reporting of research evidence. Lancet. 2009;374(9683):86–89. doi: https://doi.org/10.1016/S0140-6736(09)60329-9</mixed-citation><mixed-citation xml:lang="en">Chalmers I, Glasziou P. Avoidable waste in the production and reporting of research evidence. Lancet. 2009;374(9683):86–89. doi: https://doi.org/10.1016/S0140-6736(09)60329-9</mixed-citation></citation-alternatives></ref><ref id="cit372"><label>372</label><citation-alternatives><mixed-citation xml:lang="ru">Janssen KJ, Vergouwe Y, Kalkman CJ, et al. A simple method to adjust clinical prediction models to local circumstances. Can J Anaesth. 2009;56(3):194–201. doi: https://doi.org/10.1007/s12630-009-9041-x</mixed-citation><mixed-citation xml:lang="en">Janssen KJ, Vergouwe Y, Kalkman CJ, et al. A simple method to adjust clinical prediction models to local circumstances. Can J Anaesth. 2009;56(3):194–201. doi: https://doi.org/10.1007/s12630-009-9041-x</mixed-citation></citation-alternatives></ref><ref id="cit373"><label>373</label><citation-alternatives><mixed-citation xml:lang="ru">van Houwelingen HC. Validation. calibration, revision and combination of prognostic survival models. Stat Med. 2000;19(24): 3401–3415. doi: https://doi.org/10.1002/1097-0258(20001230)19:243.0.co;2-2</mixed-citation><mixed-citation xml:lang="en">van Houwelingen HC. Validation. calibration, revision and combination of prognostic survival models. Stat Med. 2000;19(24): 3401–3415. doi: https://doi.org/10.1002/1097-0258(20001230)19:243.0.co;2-2</mixed-citation></citation-alternatives></ref><ref id="cit374"><label>374</label><citation-alternatives><mixed-citation xml:lang="ru">Manola J, Royston P, Elson P, et al. Prognostic model for survival in patients with metastatic renal cell carcinoma: results from the International Kidney Cancer Working Group. Clin Cancer Res. 2011;17(16):5443–5450. doi: https://doi.org/10.1158/1078-0432.CCR-11-0553</mixed-citation><mixed-citation xml:lang="en">Manola J, Royston P, Elson P, et al. Prognostic model for survival in patients with metastatic renal cell carcinoma: results from the International Kidney Cancer Working Group. Clin Cancer Res. 2011;17(16):5443–5450. doi: https://doi.org/10.1158/1078-0432.CCR-11-0553</mixed-citation></citation-alternatives></ref><ref id="cit375"><label>375</label><citation-alternatives><mixed-citation xml:lang="ru">Krupp NL, Weinstein G, Chalian A, et al. Validation of a transfusion prediction model in head and neck cancer surgery. Arch Otolaryngol Head Neck Surg. 2003;129(12):1297–1302. doi: https://doi.org/10.1001/archotol.129.12.1297</mixed-citation><mixed-citation xml:lang="en">Krupp NL, Weinstein G, Chalian A, et al. Validation of a transfusion prediction model in head and neck cancer surgery. Arch Otolaryngol Head Neck Surg. 2003;129(12):1297–1302. doi: https://doi.org/10.1001/archotol.129.12.1297</mixed-citation></citation-alternatives></ref><ref id="cit376"><label>376</label><citation-alternatives><mixed-citation xml:lang="ru">Morra E, Cesana C, Klersy C, et al. Clinical characteristics and factors predicting evolution of asymptomatic IgM monoclonal gammopathies and IgM-related disorders. Leukemia. 2004;18(9):1512–1517. doi: https://doi.org/10.1038/sj.leu.2403442</mixed-citation><mixed-citation xml:lang="en">Morra E, Cesana C, Klersy C, et al. Clinical characteristics and factors predicting evolution of asymptomatic IgM monoclonal gammopathies and IgM-related disorders. Leukemia. 2004;18(9):1512–1517. doi: https://doi.org/10.1038/sj.leu.2403442</mixed-citation></citation-alternatives></ref><ref id="cit377"><label>377</label><citation-alternatives><mixed-citation xml:lang="ru">Kelder JC, Cramer MJ, van Wijngaarden J, et al. The diagnostic value of physical examination and additional testing in primary care patients with suspected heart failure. Circulation. 2011;124(25):2865–2873. doi: https://doi.org/10.1161/CIRCULATIONAHA.111.019216</mixed-citation><mixed-citation xml:lang="en">Kelder JC, Cramer MJ, van Wijngaarden J, et al. The diagnostic value of physical examination and additional testing in primary care patients with suspected heart failure. Circulation. 2011;124(25):2865–2873. doi: https://doi.org/10.1161/CIRCULATIONAHA.111.019216</mixed-citation></citation-alternatives></ref><ref id="cit378"><label>378</label><citation-alternatives><mixed-citation xml:lang="ru">Haybittle JL, Blamey RW, Elston CW, et al. A prognostic index in primary breast cancer. Br J Cancer. 1982;45(3):361–366. doi: https://doi.org/10.1038/bjc.1982.62</mixed-citation><mixed-citation xml:lang="en">Haybittle JL, Blamey RW, Elston CW, et al. A prognostic index in primary breast cancer. Br J Cancer. 1982;45(3):361–366. doi: https://doi.org/10.1038/bjc.1982.62</mixed-citation></citation-alternatives></ref><ref id="cit379"><label>379</label><citation-alternatives><mixed-citation xml:lang="ru">Tang EW, Wong CK, Herbison P. Global Registry of Acute Coronary Events (GRACE) hospital discharge risk score accurately predicts long-term mortality post acute coronary syndrome. Am Heart J. 2007;153(1):29–35. doi: https://doi.org/10.1016/j.ahj.2006.10.004</mixed-citation><mixed-citation xml:lang="en">Tang EW, Wong CK, Herbison P. Global Registry of Acute Coronary Events (GRACE) hospital discharge risk score accurately predicts long-term mortality post acute coronary syndrome. Am Heart J. 2007;153(1):29–35. doi: https://doi.org/10.1016/j.ahj.2006.10.004</mixed-citation></citation-alternatives></ref><ref id="cit380"><label>380</label><citation-alternatives><mixed-citation xml:lang="ru">Bang H, Edwards AM, Bomback AS, et al. Development and validation of a patient selfassessment score for diabetes risk. Ann Intern Med. 2009;151(11):775–783. doi: https://doi.org/10.7326/0003-4819-151-11-200912010-00005</mixed-citation><mixed-citation xml:lang="en">Bang H, Edwards AM, Bomback AS, et al. Development and validation of a patient selfassessment score for diabetes risk. Ann Intern Med. 2009;151(11):775–783. doi: https://doi.org/10.7326/0003-4819-151-11-200912010-00005</mixed-citation></citation-alternatives></ref><ref id="cit381"><label>381</label><citation-alternatives><mixed-citation xml:lang="ru">Chen L, Magliano DJ, Balkau B, et al. AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures. Med J Aust. 2010;192(4): 197–202. doi: https://doi.org/10.5694/j.1326-5377.2010.tb03507.x</mixed-citation><mixed-citation xml:lang="en">Chen L, Magliano DJ, Balkau B, et al. AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures. Med J Aust. 2010;192(4): 197–202. doi: https://doi.org/10.5694/j.1326-5377.2010.tb03507.x</mixed-citation></citation-alternatives></ref><ref id="cit382"><label>382</label><citation-alternatives><mixed-citation xml:lang="ru">Starmans R, Muris JW, Fijten GH, et al. The diagnostic value of scoring models for organic and non-organic gastrointestinal disease, including the irritable-bowel syndrome. Med Decis Making. 1994;14(3):208–216. doi: https://doi.org/10.1177/0272989X9401400302</mixed-citation><mixed-citation xml:lang="en">Starmans R, Muris JW, Fijten GH, et al. The diagnostic value of scoring models for organic and non-organic gastrointestinal disease, including the irritable-bowel syndrome. Med Decis Making. 1994;14(3):208–216. doi: https://doi.org/10.1177/0272989X9401400302</mixed-citation></citation-alternatives></ref><ref id="cit383"><label>383</label><citation-alternatives><mixed-citation xml:lang="ru">Tzoulaki I, Seretis A, Ntzani EE, Ioannidis JP. Mapping the expanded often inappropriate use of the Framingham Risk Score in the medical literature. J Clin Epidemiol. 2014;67(5):571–577. doi: https://doi.org/10.1016/j.jclinepi.2013.10.021</mixed-citation><mixed-citation xml:lang="en">Tzoulaki I, Seretis A, Ntzani EE, Ioannidis JP. Mapping the expanded often inappropriate use of the Framingham Risk Score in the medical literature. J Clin Epidemiol. 2014;67(5):571–577. doi: https://doi.org/10.1016/j.jclinepi.2013.10.021</mixed-citation></citation-alternatives></ref><ref id="cit384"><label>384</label><citation-alternatives><mixed-citation xml:lang="ru">Harrison DA, Rowan KM. Outcome prediction in critical care: the ICNARC model. Curr Opin Crit Care. 2008;14(5):506–512. doi: https://doi.org/10.1097/MCC.0b013e328310165a</mixed-citation><mixed-citation xml:lang="en">Harrison DA, Rowan KM. Outcome prediction in critical care: the ICNARC model. Curr Opin Crit Care. 2008;14(5):506–512. doi: https://doi.org/10.1097/MCC.0b013e328310165a</mixed-citation></citation-alternatives></ref><ref id="cit385"><label>385</label><citation-alternatives><mixed-citation xml:lang="ru">Kanaya AM, WasselFyr CL, de Rekeneire N, et al. Predicting the development of diabetes in older adults: the derivation and validation of a prediction rule. Diabetes Care. 2005;28(2):404–408. doi: https://doi.org/10.2337/diacare.28.2.404</mixed-citation><mixed-citation xml:lang="en">Kanaya AM, WasselFyr CL, de Rekeneire N, et al. Predicting the development of diabetes in older adults: the derivation and validation of a prediction rule. Diabetes Care. 2005;28(2):404–408. doi: https://doi.org/10.2337/diacare.28.2.404</mixed-citation></citation-alternatives></ref><ref id="cit386"><label>386</label><citation-alternatives><mixed-citation xml:lang="ru">Stephens JW, Ambler G, Vallance P, et al. Cardiovascular risk and diabetes. Are the methods of risk prediction satisfactory? Eur J Cardiovasc Prev Rehabil. 2004;11(6):521–528. doi: https://doi.org/10.1097/01.hjr.0000136418.47640.bc</mixed-citation><mixed-citation xml:lang="en">Stephens JW, Ambler G, Vallance P, et al. Cardiovascular risk and diabetes. Are the methods of risk prediction satisfactory? Eur J Cardiovasc Prev Rehabil. 2004;11(6):521–528. doi: https://doi.org/10.1097/01.hjr.0000136418.47640.bc</mixed-citation></citation-alternatives></ref><ref id="cit387"><label>387</label><citation-alternatives><mixed-citation xml:lang="ru">Cogswell R, Kobashigawa E, McGlothlin D, et al. Validation of the Registry to Evaluate Early and Long-Term Pulmonary Arterial Hypertension Disease Management (REVEAL) pulmonary hypertension prediction model in a unique population and utility in the prediction of long-term survival. J Heart Lung Transplant. 2012;31(11):1165–1170. doi: https://doi.org/10.1016/j.healun.2012.08.009</mixed-citation><mixed-citation xml:lang="en">Cogswell R, Kobashigawa E, McGlothlin D, et al. Validation of the Registry to Evaluate Early and Long-Term Pulmonary Arterial Hypertension Disease Management (REVEAL) pulmonary hypertension prediction model in a unique population and utility in the prediction of long-term survival. J Heart Lung Transplant. 2012;31(11):1165–1170. doi: https://doi.org/10.1016/j.healun.2012.08.009</mixed-citation></citation-alternatives></ref><ref id="cit388"><label>388</label><citation-alternatives><mixed-citation xml:lang="ru">Eagle KA, Lim MJ, Dabbous OH, et al. A validated prediction model for all forms of acute coronary syndrome: estimating the risk of 6-month postdischarge death in an international registry. JAMA. 2004;291(22):2727–2733. doi: https://doi.org/10.1001/jama.291.22.2727</mixed-citation><mixed-citation xml:lang="en">Eagle KA, Lim MJ, Dabbous OH, et al. A validated prediction model for all forms of acute coronary syndrome: estimating the risk of 6-month postdischarge death in an international registry. JAMA. 2004;291(22):2727–2733. doi: https://doi.org/10.1001/jama.291.22.2727</mixed-citation></citation-alternatives></ref><ref id="cit389"><label>389</label><citation-alternatives><mixed-citation xml:lang="ru">Geersing GJ, Erkens PM, Lucassen WA, et al. Safe exclusion of pulmonary embolism using the Wells rule and qualitative d-dimer testing in primary care: prospective cohort study. BMJ. 2012; 345:e6564. doi: https://doi.org/10.1136/bmj.e6564</mixed-citation><mixed-citation xml:lang="en">Geersing GJ, Erkens PM, Lucassen WA, et al. Safe exclusion of pulmonary embolism using the Wells rule and qualitative d-dimer testing in primary care: prospective cohort study. BMJ. 2012; 345:e6564. doi: https://doi.org/10.1136/bmj.e6564</mixed-citation></citation-alternatives></ref><ref id="cit390"><label>390</label><citation-alternatives><mixed-citation xml:lang="ru">Collins GS, Altman DG. Identifying patients with undetected gastro-oesophageal cancer in primary care: external validation of QCancer® (Gastro-Oesophageal). Eur J Cancer. 2013;49(5): 1040–1048. doi: https://doi.org/10.1016/j.ejca.2012.10.023</mixed-citation><mixed-citation xml:lang="en">Collins GS, Altman DG. Identifying patients with undetected gastro-oesophageal cancer in primary care: external validation of QCancer® (Gastro-Oesophageal). Eur J Cancer. 2013;49(5): 1040–1048. doi: https://doi.org/10.1016/j.ejca.2012.10.023</mixed-citation></citation-alternatives></ref><ref id="cit391"><label>391</label><citation-alternatives><mixed-citation xml:lang="ru">de Vin T, Engels B, Gevaert T, et al. Stereotactic radiotherapy for oligometastatic cancer: a prognostic model for survival. Ann Oncol. 2014;25(2):467–471. doi: https://doi.org/10.1093/annonc/mdt537</mixed-citation><mixed-citation xml:lang="en">de Vin T, Engels B, Gevaert T, et al. Stereotactic radiotherapy for oligometastatic cancer: a prognostic model for survival. Ann Oncol. 2014;25(2):467–471. doi: https://doi.org/10.1093/annonc/mdt537</mixed-citation></citation-alternatives></ref><ref id="cit392"><label>392</label><citation-alternatives><mixed-citation xml:lang="ru">Bernasconi P, Klersy C, Boni M, et al. World Health Organization classification in combination with cytogenetic markers improves the prognostic stratification of patients with de novo primary myelodysplastic syndromes. Br J Haematol. 2007;137(3):193–205. doi: https://doi.org/10.1111/j.1365-2141.2007.06537.x</mixed-citation><mixed-citation xml:lang="en">Bernasconi P, Klersy C, Boni M, et al. World Health Organization classification in combination with cytogenetic markers improves the prognostic stratification of patients with de novo primary myelodysplastic syndromes. Br J Haematol. 2007;137(3):193–205. doi: https://doi.org/10.1111/j.1365-2141.2007.06537.x</mixed-citation></citation-alternatives></ref><ref id="cit393"><label>393</label><citation-alternatives><mixed-citation xml:lang="ru">Schemper M, Smith TL. A note on quantifying follow-up in studies of failure time. Control Clin Trials. 1996;17(4):343–346. doi: https://doi.org/10.1016/0197-2456(96)00075-x</mixed-citation><mixed-citation xml:lang="en">Schemper M, Smith TL. A note on quantifying follow-up in studies of failure time. Control Clin Trials. 1996;17(4):343–346. doi: https://doi.org/10.1016/0197-2456(96)00075-x</mixed-citation></citation-alternatives></ref><ref id="cit394"><label>394</label><citation-alternatives><mixed-citation xml:lang="ru">Echouffo-Tcheugui JB, Woodward M, Kengne AP. Predicting a post-thrombolysis intracerebral hemorrhage: a systematic review. J Thromb Haemost. 2013;11(5):862–871. doi: https://doi.org/10.1111/jth.12186</mixed-citation><mixed-citation xml:lang="en">Echouffo-Tcheugui JB, Woodward M, Kengne AP. Predicting a post-thrombolysis intracerebral hemorrhage: a systematic review. J Thromb Haemost. 2013;11(5):862–871. doi: https://doi.org/10.1111/jth.12186</mixed-citation></citation-alternatives></ref><ref id="cit395"><label>395</label><citation-alternatives><mixed-citation xml:lang="ru">Le Gal G, Righini M, Roy PM, et al. Prediction of pulmonary embolism in the emergency department: the revised Geneva score. Ann Intern Med. 2006;144(3):165–171. doi: https://doi.org/10.7326/0003-4819-144-3-200602070-00004</mixed-citation><mixed-citation xml:lang="en">Le Gal G, Righini M, Roy PM, et al. Prediction of pulmonary embolism in the emergency department: the revised Geneva score. Ann Intern Med. 2006;144(3):165–171. doi: https://doi.org/10.7326/0003-4819-144-3-200602070-00004</mixed-citation></citation-alternatives></ref><ref id="cit396"><label>396</label><citation-alternatives><mixed-citation xml:lang="ru">Davis JL, Worodria W, Kisembo H, et al. Clinical and radiographic factors do not accurately diagnose smear-negative tuberculosis in HIV-infected inpatients in Uganda: a cross-sectional study. PLoS One. 2010;5(3):e9859. doi: https://doi.org/10.1371/journal.pone.0009859</mixed-citation><mixed-citation xml:lang="en">Davis JL, Worodria W, Kisembo H, et al. Clinical and radiographic factors do not accurately diagnose smear-negative tuberculosis in HIV-infected inpatients in Uganda: a cross-sectional study. PLoS One. 2010;5(3):e9859. doi: https://doi.org/10.1371/journal.pone.0009859</mixed-citation></citation-alternatives></ref><ref id="cit397"><label>397</label><citation-alternatives><mixed-citation xml:lang="ru">Ji R, Shen H, Pan Y, et al. Risk score to predict gastrointestinal bleeding after acute ischemic stroke. BMC Gastroenterol. 2014; 14:130. doi: https://doi.org/10.1186/1471-230X-14-130</mixed-citation><mixed-citation xml:lang="en">Ji R, Shen H, Pan Y, et al. Risk score to predict gastrointestinal bleeding after acute ischemic stroke. BMC Gastroenterol. 2014; 14:130. doi: https://doi.org/10.1186/1471-230X-14-130</mixed-citation></citation-alternatives></ref><ref id="cit398"><label>398</label><citation-alternatives><mixed-citation xml:lang="ru">Marrugat J, Subirana I, Ramos R, et al. Derivation and validation of a set of 10-year cardiovascular risk predictive functions in Spain: the FRESCO Study. Prev Med. 2014;61:66–74. doi: https://doi.org/10.1016/j.ypmed.2013.12.031</mixed-citation><mixed-citation xml:lang="en">Marrugat J, Subirana I, Ramos R, et al. Derivation and validation of a set of 10-year cardiovascular risk predictive functions in Spain: the FRESCO Study. Prev Med. 2014;61:66–74. doi: https://doi.org/10.1016/j.ypmed.2013.12.031</mixed-citation></citation-alternatives></ref><ref id="cit399"><label>399</label><citation-alternatives><mixed-citation xml:lang="ru">Hensgens MP, Dekkers OM, Goorhuis A, et al. Predicting a complicated course of Clostridium difficile infection at the bedside. Clin Microbiol Infect. 2014;20(5):O301–O308. doi: https://doi.org/10.1111/1469-0691.12391</mixed-citation><mixed-citation xml:lang="en">Hensgens MP, Dekkers OM, Goorhuis A, et al. Predicting a complicated course of Clostridium difficile infection at the bedside. Clin Microbiol Infect. 2014;20(5):O301–O308. doi: https://doi.org/10.1111/1469-0691.12391</mixed-citation></citation-alternatives></ref><ref id="cit400"><label>400</label><citation-alternatives><mixed-citation xml:lang="ru">Hak E, Wei F, Nordin J, et al. Development and validation of a clinical prediction rule for hospitalization due to pneumonia or influenza or death during influenza epidemics among communitydwelling elderly persons. J Infect Dis. 2004;189(3):450–458. doi: https://doi.org/10.1086/381165</mixed-citation><mixed-citation xml:lang="en">Hak E, Wei F, Nordin J, et al. Development and validation of a clinical prediction rule for hospitalization due to pneumonia or influenza or death during influenza epidemics among communitydwelling elderly persons. J Infect Dis. 2004;189(3):450–458. doi: https://doi.org/10.1086/381165</mixed-citation></citation-alternatives></ref><ref id="cit401"><label>401</label><citation-alternatives><mixed-citation xml:lang="ru">Vandenbroucke JP, von Elm E, Altman DG, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. Epidemiology. 2007;18(6):805–835. doi: https://doi.org/10.1097/EDE.0b013e3181577511</mixed-citation><mixed-citation xml:lang="en">Vandenbroucke JP, von Elm E, Altman DG, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. Epidemiology. 2007;18(6):805–835. doi: https://doi.org/10.1097/EDE.0b013e3181577511</mixed-citation></citation-alternatives></ref><ref id="cit402"><label>402</label><citation-alternatives><mixed-citation xml:lang="ru">Schnabel RB, Sullivan LM, Levy D, et al. Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study. Lancet. 2009;373(9665): 739–745. doi: https://doi.org/10.1016/S0140-6736(09)60443-8</mixed-citation><mixed-citation xml:lang="en">Schnabel RB, Sullivan LM, Levy D, et al. Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study. Lancet. 2009;373(9665): 739–745. doi: https://doi.org/10.1016/S0140-6736(09)60443-8</mixed-citation></citation-alternatives></ref><ref id="cit403"><label>403</label><citation-alternatives><mixed-citation xml:lang="ru">Lang TA, Altman DG. Basic statistical reporting for articles published in clinical medical journals: the SAMPL guidelines. In: Science Editors’ Handbook. Smart P, Maisonneuve H, Polderman A, eds. European Association of Science Editors; 2013.</mixed-citation><mixed-citation xml:lang="en">Lang TA, Altman DG. Basic statistical reporting for articles published in clinical medical journals: the SAMPL guidelines. In: Science Editors’ Handbook. Smart P, Maisonneuve H, Polderman A, eds. European Association of Science Editors; 2013.</mixed-citation></citation-alternatives></ref><ref id="cit404"><label>404</label><citation-alternatives><mixed-citation xml:lang="ru">Binder H, Sauerbrei W, Royston P. Comparison between splines and fractional polynomials for multivariable model building with continuous covariates: a simulation study with continuous response. Stat Med. 2013;32(13):2262–2277. doi: https://doi.org/10.1002/sim.5639</mixed-citation><mixed-citation xml:lang="en">Binder H, Sauerbrei W, Royston P. Comparison between splines and fractional polynomials for multivariable model building with continuous covariates: a simulation study with continuous response. Stat Med. 2013;32(13):2262–2277. doi: https://doi.org/ 10.1002/sim.5639</mixed-citation></citation-alternatives></ref><ref id="cit405"><label>405</label><citation-alternatives><mixed-citation xml:lang="ru">Harrison DA, Parry GJ, Carpenter JR, et al. A new risk prediction model for critical care: the Intensive Care National Audit &amp; Research Centre (ICNARC) model. Crit Care Med. 2007;35(4):1091–1098. doi: https://doi.org/10.1097/01.CCM.0000259468.24532.44</mixed-citation><mixed-citation xml:lang="en">Harrison DA, Parry GJ, Carpenter JR, et al. A new risk prediction model for critical care: the Intensive Care National Audit &amp; Research Centre (ICNARC) model. Crit Care Med. 2007;35(4):1091–1098. doi: https://doi.org/10.1097/01.CCM.0000259468.24532.44</mixed-citation></citation-alternatives></ref><ref id="cit406"><label>406</label><citation-alternatives><mixed-citation xml:lang="ru">Brady AR, Harrison D, Black S, et al. Assessment and optimization of mortality prediction tools for admissions to pediatric intensive care in the United Kingdom. Pediatrics. 2006;117(4): e733–e742. doi: https://doi.org/10.1542/peds.2005-1853</mixed-citation><mixed-citation xml:lang="en">Brady AR, Harrison D, Black S, et al. Assessment and optimization of mortality prediction tools for admissions to pediatric intensive care in the United Kingdom. Pediatrics. 2006;117(4): e733–e742. doi: https://doi.org/10.1542/peds.2005-1853</mixed-citation></citation-alternatives></ref><ref id="cit407"><label>407</label><citation-alternatives><mixed-citation xml:lang="ru">Kuijpers T, van der Windt DA, van der Heijden GJ, et al. A prediction rule for shoulder pain related sick leave: a prospective cohort study. BMC Musculoskelet Disord. 2006;7:97. doi: https://doi.org/10.1186/1471-2474-7-97</mixed-citation><mixed-citation xml:lang="en">Kuijpers T, van der Windt DA, van der Heijden GJ, et al. A prediction rule for shoulder pain related sick leave: a prospective cohort study. BMC Musculoskelet Disord. 2006;7:97. doi: https://doi.org/10.1186/1471-2474-7-97</mixed-citation></citation-alternatives></ref><ref id="cit408"><label>408</label><citation-alternatives><mixed-citation xml:lang="ru">Pocock SJ, McCormack V, Gueyffier F, et al. A score for predicting risk of death from cardiovascular disease in adults with raised blood pressure, based on individual patient data from randomised controlled trials. BMJ. 2001;323(7304):75–81. doi: https://doi.org/10.1136/bmj.323.7304.75</mixed-citation><mixed-citation xml:lang="en">Pocock SJ, McCormack V, Gueyffier F, et al. A score for predicting risk of death from cardiovascular disease in adults with raised blood pressure, based on individual patient data from randomised controlled trials. BMJ. 2001;323(7304):75–81. doi: https://doi.org/10.1136/bmj.323.7304.75</mixed-citation></citation-alternatives></ref><ref id="cit409"><label>409</label><citation-alternatives><mixed-citation xml:lang="ru">Casikar I, Lu C, Reid S, Condous G. Prediction of successful expec tant management of first trimester miscarriage: development and validation of a new mathematical model. Aust N Z J Obstet Gynaecol. 2013;53(1):58–63. doi: https://doi.org/10.1111/ajo.12053</mixed-citation><mixed-citation xml:lang="en">Casikar I, Lu C, Reid S, Condous G. Prediction of successful expec tant management of first trimester miscarriage: development and validation of a new mathematical model. Aust N Z J Obstet Gynaecol. 2013;53(1):58–63. doi: https://doi.org/10.1111/ajo.12053</mixed-citation></citation-alternatives></ref><ref id="cit410"><label>410</label><citation-alternatives><mixed-citation xml:lang="ru">Godoy G, Chong KT, Cronin A, et al. Extent of pelvic lymph node dissection and the impact of standard template dissection on nomogram prediction of lymph node involvement. Eur Urol. 2011;60(2):195–201. doi: https://doi.org/10.1016/j.eururo.2011.01.016</mixed-citation><mixed-citation xml:lang="en">Godoy G, Chong KT, Cronin A, et al. Extent of pelvic lymph node dissection and the impact of standard template dissection on nomogram prediction of lymph node involvement. Eur Urol. 2011;60(2):195–201. doi: https://doi.org/10.1016/j.eururo.2011.01.016</mixed-citation></citation-alternatives></ref><ref id="cit411"><label>411</label><citation-alternatives><mixed-citation xml:lang="ru">Bradburn MJ, Clark TG, Love SB, Altman DG. Survival analysis part II: multivariate data analysis — an introduction to concepts and methods. Br J Cancer. 2003;89(3):431–436. doi: https://doi.org/10.1038/sj.bjc.6601119</mixed-citation><mixed-citation xml:lang="en">Bradburn MJ, Clark TG, Love SB, Altman DG. Survival analysis part II: multivariate data analysis — an introduction to concepts and methods. Br J Cancer. 2003;89(3):431–436. doi: https://doi.org/10.1038/sj.bjc.6601119</mixed-citation></citation-alternatives></ref><ref id="cit412"><label>412</label><citation-alternatives><mixed-citation xml:lang="ru">Wells P, Anderson D, Rodger M, et al. Derivation of a simple clinical model to categorize patients probability of pulmonary embolism: increasing the models utility with the SimpliRED D-dimer. Thromb Haemost. 2000;83(3):416–420.</mixed-citation><mixed-citation xml:lang="en">Wells P, Anderson D, Rodger M, et al. Derivation of a simple clinical model to categorize patients probability of pulmonary embolism: increasing the models utility with the SimpliRED D-dimer. Thromb Haemost. 2000;83(3):416–420.</mixed-citation></citation-alternatives></ref><ref id="cit413"><label>413</label><citation-alternatives><mixed-citation xml:lang="ru">Cole TJ. Scaling and rounding regression coefficients to integers. Appl Stat. 1993;42(1):261–268. doi: https://doi.org/10.2307/2347432</mixed-citation><mixed-citation xml:lang="en">Cole TJ. Scaling and rounding regression coefficients to integers. Appl Stat. 1993;42(1):261–268. doi: https://doi.org/10.2307/2347432</mixed-citation></citation-alternatives></ref><ref id="cit414"><label>414</label><citation-alternatives><mixed-citation xml:lang="ru">Sullivan LM, Massaro JM, D’Agostino RB. Presentation of multivariate data for clinical use: the Framingham study risk score functions. Stat Med. 2004;23(10):1631–1660. doi: https://doi.org/10.1002/sim.1742</mixed-citation><mixed-citation xml:lang="en">Sullivan LM, Massaro JM, D’Agostino RB. Presentation of multivariate data for clinical use: the Framingham study risk score functions. Stat Med. 2004;23(10):1631–1660. doi: https://doi.org/10.1002/sim.1742</mixed-citation></citation-alternatives></ref><ref id="cit415"><label>415</label><citation-alternatives><mixed-citation xml:lang="ru">Moons KG, Harrell FE, Steyerberg EW. Should scoring rules be based on odds rati os or regression coefficients? J Clin Epidemiol. 2002;55(10):1054–1055. doi: https://doi.org/10.1016/s0895-4356(02)00453-5</mixed-citation><mixed-citation xml:lang="en">Moons KG, Harrell FE, Steyerberg EW. Should scoring rules be based on odds rati os or regression coefficients? J Clin Epidemiol. 2002;55(10):1054–1055. doi: https://doi.org/10.1016/s0895-4356(02)00453-5</mixed-citation></citation-alternatives></ref><ref id="cit416"><label>416</label><citation-alternatives><mixed-citation xml:lang="ru">Nijman RG, Vergouwe Y, Thompson M, et al. Clinical prediction model to aid emergency doctors managing febrile children at risk of serious bacterial infections: diagnostic study. BMJ. 2013;346:f1706. doi: https://doi.org/10.1136/bmj.f1706</mixed-citation><mixed-citation xml:lang="en">Nijman RG, Vergouwe Y, Thompson M, et al. Clinical prediction model to aid emergency doctors managing febrile children at risk of serious bacterial infections: diagnostic study. BMJ. 2013;346:f1706. doi: https://doi.org/10.1136/bmj.f1706</mixed-citation></citation-alternatives></ref><ref id="cit417"><label>417</label><citation-alternatives><mixed-citation xml:lang="ru">Royston P, Altman DG. Visualizing and assessing discrimina tion in the logistic regression model. Stat Med. 2010;29(24): 2508–2520. doi: https://doi.org/10.1002/sim.3994</mixed-citation><mixed-citation xml:lang="en">Royston P, Altman DG. Visualizing and assessing discrimina tion in the logistic regression model. Stat Med. 2010;29(24): 2508–2520. doi: https://doi.org/10.1002/sim.3994</mixed-citation></citation-alternatives></ref><ref id="cit418"><label>418</label><citation-alternatives><mixed-citation xml:lang="ru">Taş U, Steyerberg EW, Bierma-Zeinstra SM, et al. Age, gender and disability predict future disability in older people: the Rotterdam Study. BMC Geriatrics. 2011;11:22. doi: https://doi.org/10.1186/1471-2318-11-22</mixed-citation><mixed-citation xml:lang="en">Taş U, Steyerberg EW, Bierma-Zeinstra SM, et al. Age, gender and disability predict future disability in older people: the Rotterdam Study. BMC Geriatrics. 2011;11:22. doi: https://doi.org/10.1186/1471-2318-11-22</mixed-citation></citation-alternatives></ref><ref id="cit419"><label>419</label><citation-alternatives><mixed-citation xml:lang="ru">Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 1983;148(3):839–843. doi: https://doi.org/10.1148/radiology.148.3.6878708</mixed-citation><mixed-citation xml:lang="en">Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 1983;148(3):839–843. doi: https://doi.org/10.1148/radiology.148.3.6878708</mixed-citation></citation-alternatives></ref><ref id="cit420"><label>420</label><citation-alternatives><mixed-citation xml:lang="ru">Pencina MJ, D’Agostino RB, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011;30(1):11–21. doi: https://doi.org/10.1002/sim.4085</mixed-citation><mixed-citation xml:lang="en">Pencina MJ, D’Agostino RB, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011;30(1):11–21. doi: https://doi.org/10.1002/sim.4085</mixed-citation></citation-alternatives></ref><ref id="cit421"><label>421</label><citation-alternatives><mixed-citation xml:lang="ru">Pepe MS, Janes H. Reporting standards are needed for evaluations of risk reclassification. Int J Epidemiol. 2011;40(4): 1106–1108. doi: https://doi.org/10.1093/ije/dyr083</mixed-citation><mixed-citation xml:lang="en">Pepe MS, Janes H. Reporting standards are needed for evaluations of risk reclassification. Int J Epidemiol. 2011;40(4): 1106–1108. doi: https://doi.org/10.1093/ije/dyr083</mixed-citation></citation-alternatives></ref><ref id="cit422"><label>422</label><citation-alternatives><mixed-citation xml:lang="ru">Vickers AJ, Cronin AM. Traditional statistical methods for evaluating prediction models are uninformative as to clinical value: towards a decision analytic framework. Semin Oncol. 2010;37(1):31–38. doi: https://doi.org/10.1053/j.seminoncol.2009.12.004</mixed-citation><mixed-citation xml:lang="en">Vickers AJ, Cronin AM. Traditional statistical methods for evaluating prediction models are uninformative as to clinical value: towards a decision analytic framework. Semin Oncol. 2010;37(1):31–38. doi: https://doi.org/10.1053/j.seminoncol.2009.12.004</mixed-citation></citation-alternatives></ref><ref id="cit423"><label>423</label><citation-alternatives><mixed-citation xml:lang="ru">Sanders MS, de Jonge RC, Terwee CB, et al. Addition of host genetic variants in a prediction rule for post meningitis hearing loss in childhood: a model updating study. BMC Infect Dis. 2013;13:340. doi: https://doi.org/10.1186/1471-2334-13-340</mixed-citation><mixed-citation xml:lang="en">Sanders MS, de Jonge RC, Terwee CB, et al. Addition of host genetic variants in a prediction rule for post meningitis hearing loss in childhood: a model updating study. BMC Infect Dis. 2013;13:340. doi: https://doi.org/10.1186/1471-2334-13-340</mixed-citation></citation-alternatives></ref><ref id="cit424"><label>424</label><citation-alternatives><mixed-citation xml:lang="ru">Kramer AA, Zimmerman JE. A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay. BMC Med Inform Decis Mak. 2010;10:27. doi: https://doi.org/10.1186/1472-6947-10-27</mixed-citation><mixed-citation xml:lang="en">Kramer AA, Zimmerman JE. A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay. BMC Med Inform Decis Mak. 2010;10:27. doi: https://doi.org/10.1186/1472-6947-10-27</mixed-citation></citation-alternatives></ref><ref id="cit425"><label>425</label><citation-alternatives><mixed-citation xml:lang="ru">Neely D, Feinglass J, Wallace WH. Developing a predictive model to assess applicants to an internal medicine residency. J Grad Med Educ. 2010;2(1):129–132. doi: https://doi.org/10.4300/JGME-D-09-00044.1</mixed-citation><mixed-citation xml:lang="en">Neely D, Feinglass J, Wallace WH. Developing a predictive model to assess applicants to an internal medicine residency. J Grad Med Educ. 2010;2(1):129–132. doi: https://doi.org/10.4300/JGME-D-09-00044.1</mixed-citation></citation-alternatives></ref><ref id="cit426"><label>426</label><citation-alternatives><mixed-citation xml:lang="ru">Ioannidis JP. Limitations are not properly acknowledged in the scientific literature. J Clin Epidemiol. 2007;60(4):324–329. doi: https://doi.org/10.1016/j.jclinepi.2006.09.011</mixed-citation><mixed-citation xml:lang="en">Ioannidis JP. Limitations are not properly acknowledged in the scientific literature. J Clin Epidemiol. 2007;60(4):324–329. doi: https://doi.org/10.1016/j.jclinepi.2006.09.011</mixed-citation></citation-alternatives></ref><ref id="cit427"><label>427</label><citation-alternatives><mixed-citation xml:lang="ru">Horton R. The hidden research paper. JAMA. 2002;287(21): 2775–2778. doi: https://doi.org/10.1001/jama.287.21.2775</mixed-citation><mixed-citation xml:lang="en">Horton R. The hidden research paper. JAMA. 2002;287(21): 2775–2778. doi: https://doi.org/10.1001/jama.287.21.2775</mixed-citation></citation-alternatives></ref><ref id="cit428"><label>428</label><citation-alternatives><mixed-citation xml:lang="ru">Docherty M, Smith R. The case for structuring the discussion of scientific papers. BMJ. 1999;318(7193):1224–1225. doi: https://doi.org/10.1136/bmj.318.7193.1224</mixed-citation><mixed-citation xml:lang="en">Docherty M, Smith R. The case for structuring the discussion of scientific papers. BMJ. 1999;318(7193):1224–1225. doi: https://doi.org/10.1136/bmj.318.7193.1224</mixed-citation></citation-alternatives></ref><ref id="cit429"><label>429</label><citation-alternatives><mixed-citation xml:lang="ru">Ioannidis JP. Research needs grants, funding and money — missing something? Eur J Clin Invest. 2012;42(4):349–351. doi: https://doi.org/10.1111/j.1365-2362.2011.02617.x</mixed-citation><mixed-citation xml:lang="en">Ioannidis JP. Research needs grants, funding and money — missing something? Eur J Clin Invest. 2012;42(4):349–351. doi: https://doi.org/10.1111/j.1365-2362.2011.02617.x</mixed-citation></citation-alternatives></ref><ref id="cit430"><label>430</label><citation-alternatives><mixed-citation xml:lang="ru">Janssens AC, Ioannidis JP, Bedrosian S, et al. Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration. Eur J Clin Invest. 2011;41(9):1010–1035. doi: https://doi.org/10.1111/j.1365-2362.2011.02493.x</mixed-citation><mixed-citation xml:lang="en">Janssens AC, Ioannidis JP, Bedrosian S, et al. Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration. Eur J Clin Invest. 2011;41(9):1010–1035. doi: https://doi.org/10.1111/j.1365-2362.2011.02493.x</mixed-citation></citation-alternatives></ref><ref id="cit431"><label>431</label><citation-alternatives><mixed-citation xml:lang="ru">Collins GS. Cardiovascular disease risk prediction in the UK. Primary Care Cardiovascular Journal. 2013;6:125–128.</mixed-citation><mixed-citation xml:lang="en">Collins GS. Cardiovascular disease risk prediction in the UK. Primary Care Cardiovascular Journal. 2013;6:125–128.</mixed-citation></citation-alternatives></ref><ref id="cit432"><label>432</label><citation-alternatives><mixed-citation xml:lang="ru">Collins GS, Altman DG. An independent external validation and evaluation of QRISK cardiovascular risk prediction: a prospective open cohort study. BMJ. 2009;339:b2584. doi: https://doi.org/10.1136/bmj.b2584</mixed-citation><mixed-citation xml:lang="en">Collins GS, Altman DG. An independent external validation and evaluation of QRISK cardiovascular risk prediction: a prospective open cohort study. BMJ. 2009;339:b2584. doi: https://doi.org/10.1136/bmj.b2584</mixed-citation></citation-alternatives></ref><ref id="cit433"><label>433</label><citation-alternatives><mixed-citation xml:lang="ru">Collins GS, Altman DG. An independent and external validation of QRISK2 cardiovascular disease risk score: a prospective open cohort study. BMJ. 2010;340:c2442. doi: https://doi.org/10.1136/bmj.c2442</mixed-citation><mixed-citation xml:lang="en">Collins GS, Altman DG. An independent and external validation of QRISK2 cardiovascular disease risk score: a prospective open cohort study. BMJ. 2010;340:c2442. doi: https://doi.org/10.1136/bmj.c2442</mixed-citation></citation-alternatives></ref><ref id="cit434"><label>434</label><citation-alternatives><mixed-citation xml:lang="ru">Perry JJ, Sharma M, Sivilotti ML, et al. Prospective validation of the ABCD2 score for patients in the emergency department with transient ischemic attack. CMAJ. 2011;183(10):1137–1145. doi: https://doi.org/10.1503/cmaj.101668</mixed-citation><mixed-citation xml:lang="en">Perry JJ, Sharma M, Sivilotti ML, et al. Prospective validation of the ABCD2 score for patients in the emergency department with transient ischemic attack. CMAJ. 2011;183(10):1137–1145. doi: https://doi.org/10.1503/cmaj.101668</mixed-citation></citation-alternatives></ref><ref id="cit435"><label>435</label><citation-alternatives><mixed-citation xml:lang="ru">Clarke M, Chalmers I. Discussion sections in reports of controlled trials published in general medical journals: islands in search of continents? JAMA. 1998;280(3):280–282. doi: https://doi.org/10.1001/jama.280.3.280</mixed-citation><mixed-citation xml:lang="en">Clarke M, Chalmers I. Discussion sections in reports of controlled trials published in general medical journals: islands in search of continents? JAMA. 1998;280(3):280–282. doi: https://doi.org/10.1001/jama.280.3.280</mixed-citation></citation-alternatives></ref><ref id="cit436"><label>436</label><citation-alternatives><mixed-citation xml:lang="ru">Ioannidis JP, Polyzos NP, Trikalinos TA. Selective discussion and transparency in microarray research findings for cancer outcomes. Eur J Cancer. 2007;43(13):1999–2010. doi: https://doi.org/10.1016/j.ejca.2007.05.019</mixed-citation><mixed-citation xml:lang="en">Ioannidis JP, Polyzos NP, Trikalinos TA. Selective discussion and transparency in microarray research findings for cancer outcomes. Eur J Cancer. 2007;43(13):1999–2010. doi: https://doi.org/10.1016/j.ejca.2007.05.019</mixed-citation></citation-alternatives></ref><ref id="cit437"><label>437</label><citation-alternatives><mixed-citation xml:lang="ru">Van den Bosch JE, Moons KG, Bonsel GJ, Kalkman CJ. Does measurement of preoperative anxiety have added value for predicting postoperative nausea and vomiting? Anesth Analg. 2005;100(5):1525–1532. doi: https://doi.org/10.1213/01.ANE.0000149325.20542.D4</mixed-citation><mixed-citation xml:lang="en">Van den Bosch JE, Moons KG, Bonsel GJ, Kalkman CJ. Does measurement of preoperative anxiety have added value for predicting postoperative nausea and vomiting? Anesth Analg. 2005;100(5):1525–1532. doi: https://doi.org/10.1213/01.ANE.0000149325.20542.D4</mixed-citation></citation-alternatives></ref><ref id="cit438"><label>438</label><citation-alternatives><mixed-citation xml:lang="ru">Kappen TH, Moons KG, van Wolfswinkel L, et al. Impact of risk assessments on prophylactic antiemetic prescription and the incidence of postoperative nausea and vomiting: a clusterrandomized trial. Anesthesiology. 2014;120(2):343–354. doi: https://doi.org/10.1097/ALN.0000000000000009</mixed-citation><mixed-citation xml:lang="en">Kappen TH, Moons KG, van Wolfswinkel L, et al. Impact of risk assessments on prophylactic antiemetic prescription and the incidence of postoperative nausea and vomiting: a clusterrandomized trial. Anesthesiology. 2014;120(2):343–354. doi: https://doi.org/10.1097/ALN.0000000000000009</mixed-citation></citation-alternatives></ref><ref id="cit439"><label>439</label><citation-alternatives><mixed-citation xml:lang="ru">Poldervaart JM, Reitsma JB, Koffijberg H, et al. The impact of the HEART risk score in the early assessment of patients with acute chest pain: design of a stepped wedge, cluster randomised trial. BMC Cardiovasc Disord. 2013;13:77. doi: https://doi.org/10.1186/1471-2261-13-77</mixed-citation><mixed-citation xml:lang="en">Poldervaart JM, Reitsma JB, Koffijberg H, et al. The impact of the HEART risk score in the early assessment of patients with acute chest pain: design of a stepped wedge, cluster randomised trial. BMC Cardiovasc Disord. 2013;13:77. doi: https://doi.org/10.1186/1471-2261-13-77</mixed-citation></citation-alternatives></ref><ref id="cit440"><label>440</label><citation-alternatives><mixed-citation xml:lang="ru">Hutchings HA, Evans BA, Fitzsimmons D, et al. Predictive risk stratification model: a progressive cluster-randomised trial in chronic conditions management (PRISMATIC) research protocol. Trials. 2013; 14:301. doi: https://doi.org/10.1186/1745-6215-14-301</mixed-citation><mixed-citation xml:lang="en">Hutchings HA, Evans BA, Fitzsimmons D, et al. Predictive risk stratification model: a progressive cluster-randomised trial in chronic conditions management (PRISMATIC) research protocol. Trials. 2013; 14:301. doi: https://doi.org/10.1186/1745-6215-14-301</mixed-citation></citation-alternatives></ref><ref id="cit441"><label>441</label><citation-alternatives><mixed-citation xml:lang="ru">Ioannidis JP. More than a billion people taking statins? Potential implications of the new cardiovascular guidelines. JAMA. 2014;311(5):463–464. doi: https://doi.org/10.1001/jama.2013.284657</mixed-citation><mixed-citation xml:lang="en">Ioannidis JP. More than a billion people taking statins? Potential implications of the new cardiovascular guidelines. JAMA. 2014;311(5):463–464. doi: https://doi.org/10.1001/jama.2013.284657</mixed-citation></citation-alternatives></ref><ref id="cit442"><label>442</label><citation-alternatives><mixed-citation xml:lang="ru">Ioannidis JP, Tzoulaki I. What makes a good predictor? The evidence applied to coronary artery calcium score. JAMA. 2010; 303(16):1646–1647. doi: https://doi.org/10.1001/jama.2010.503</mixed-citation><mixed-citation xml:lang="en">Ioannidis JP, Tzoulaki I. What makes a good predictor? The evidence applied to coronary artery calcium score. JAMA. 2010; 303(16):1646–1647. doi: https://doi.org/10.1001/jama.2010.503</mixed-citation></citation-alternatives></ref><ref id="cit443"><label>443</label><citation-alternatives><mixed-citation xml:lang="ru">Mrdovic I, Savic L, Krljanac G, et al. Predicting 30-day major adverse cardiovascular events after primary percutaneous coronary intervention. The RISK-PCI score. Int J Cardiol. 2013;162(3): 220–227. doi: https://doi.org/10.1016/j.ijcard.2011.05.071</mixed-citation><mixed-citation xml:lang="en">Mrdovic I, Savic L, Krljanac G, et al. Predicting 30-day major adverse cardiovascular events after primary percutaneous coronary intervention. The RISK-PCI score. Int J Cardiol. 2013;162(3): 220–227. doi: https://doi.org/10.1016/j.ijcard.2011.05.071</mixed-citation></citation-alternatives></ref><ref id="cit444"><label>444</label><citation-alternatives><mixed-citation xml:lang="ru">Ridker PM, Paynter NP, Rifai N, et al. C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation. 2008;118(22):2243–2251. doi: https://doi.org/10.1161/CIRCULATIONAHA.108.814251</mixed-citation><mixed-citation xml:lang="en">Ridker PM, Paynter NP, Rifai N, et al. C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation. 2008;118(22):2243–2251. doi: https://doi.org/10.1161/CIRCULATIONAHA.108.814251</mixed-citation></citation-alternatives></ref><ref id="cit445"><label>445</label><citation-alternatives><mixed-citation xml:lang="ru">World Medical Association. Declaration of Geneva. Available online: www.wma.net/en/30publications/10policies/g1. Accessed on June 24, 2008.</mixed-citation><mixed-citation xml:lang="en">World Medical Association. Declaration of Geneva. Available online: www.wma.net/en/30publications/10policies/g1. Accessed on June 24, 2008.</mixed-citation></citation-alternatives></ref><ref id="cit446"><label>446</label><citation-alternatives><mixed-citation xml:lang="ru">Council for International Organizations of Medical Sciences. International ethical guidelines for biomedical research involving human subjects. Bull Med Ethics. 2002;(182):17–23.</mixed-citation><mixed-citation xml:lang="en">Council for International Organizations of Medical Sciences. International ethical guidelines for biomedical research involving human subjects. Bull Med Ethics. 2002;(182):17–23.</mixed-citation></citation-alternatives></ref><ref id="cit447"><label>447</label><citation-alternatives><mixed-citation xml:lang="ru">Arnold DH, Gebretsadik T, Abramo TJ, et al. The Acute Asthma Severity Assessment Protocol (AASAP) study: objectives and methods of a study to develop an acute asthma clinical prediction rule. Emerg Med J. 2012;29(6):444–450. doi: https://doi.org/10.1136/emj.2010.110957</mixed-citation><mixed-citation xml:lang="en">Arnold DH, Gebretsadik T, Abramo TJ, et al. The Acute Asthma Severity Assessment Protocol (AASAP) study: objectives and methods of a study to develop an acute asthma clinical prediction rule. Emerg Med J. 2012;29(6):444–450. doi: https://doi.org/10.1136/emj.2010.110957</mixed-citation></citation-alternatives></ref><ref id="cit448"><label>448</label><citation-alternatives><mixed-citation xml:lang="ru">Azagra R, Roca G, Encabo G, et al. Prediction of absolute risk of fragility fracture at 10 years in a Spanish population: validation of the WHO FRAX tool in Spain. BMC Musculoskelet Disord. 2011;12:30. doi: https://doi.org/10.1186/1471-2474-12-30</mixed-citation><mixed-citation xml:lang="en">Azagra R, Roca G, Encabo G, et al. Prediction of absolute risk of fragility fracture at 10 years in a Spanish population: validation of the WHO FRAX tool in Spain. BMC Musculoskelet Disord. 2011;12:30. doi: https://doi.org/10.1186/1471-2474-12-30</mixed-citation></citation-alternatives></ref><ref id="cit449"><label>449</label><citation-alternatives><mixed-citation xml:lang="ru">Collins SP, Lindsell CJ, Jenkins CA, et al. Risk stratification in acute heart failure: rationale and design of the STRATIFY and DECIDE studies. Am Heart J. 2012;164(6):825–834. doi: https://doi.org/10.1016/j.ahj.2012.07.033</mixed-citation><mixed-citation xml:lang="en">Collins SP, Lindsell CJ, Jenkins CA, et al. Risk stratification in acute heart failure: rationale and design of the STRATIFY and DECIDE studies. Am Heart J. 2012;164(6):825–834. doi: https://doi.org/10.1016/j.ahj.2012.07.033</mixed-citation></citation-alternatives></ref><ref id="cit450"><label>450</label><citation-alternatives><mixed-citation xml:lang="ru">Hafkamp-de Groen E, Lingsma HF, Caudri D, et al. Predicting asthma in preschool children with asthma symptoms: study rationale and design. BMC Pulm Med. 2012;12:65. doi: https://doi.org/10.1186/1471-2466-12-65</mixed-citation><mixed-citation xml:lang="en">Hafkamp-de Groen E, Lingsma HF, Caudri D, et al. Predicting asthma in preschool children with asthma symptoms: study rationale and design. BMC Pulm Med. 2012;12:65. doi: https://doi.org/10.1186/1471-2466-12-65</mixed-citation></citation-alternatives></ref><ref id="cit451"><label>451</label><citation-alternatives><mixed-citation xml:lang="ru">Hess EP, Wells GA, Jaffe A, Stiell IG. A study to derive a clinical decision rule for triage of emergency department patients with chest pain: design and methodology. BMC Emerg Med. 2008;8:3. doi: https://doi.org/10.1186/1471-227X-8-3</mixed-citation><mixed-citation xml:lang="en">Hess EP, Wells GA, Jaffe A, Stiell IG. A study to derive a clinical decision rule for triage of emergency department patients with chest pain: design and methodology. BMC Emerg Med. 2008;8:3. doi: https://doi.org/10.1186/1471-227X-8-3</mixed-citation></citation-alternatives></ref><ref id="cit452"><label>452</label><citation-alternatives><mixed-citation xml:lang="ru">Horisberger T, Harbarth S, Nadal D, et al. G-CSF and IL-8 for early diagnosis of sepsis in neonates and critically ill children — safety and cost effectiveness of a new laboratory prediction model: study protocol of a randomized controlled trial [ISRCTN91123847]. Crit Care. 2004;8:R443–R450. doi: https://doi.org/10.1186/cc2971</mixed-citation><mixed-citation xml:lang="en">Horisberger T, Harbarth S, Nadal D, et al. G-CSF and IL-8 for early diagnosis of sepsis in neonates and critically ill children — safety and cost effectiveness of a new laboratory prediction model: study protocol of a randomized controlled trial [ISRCTN91123847]. Crit Care. 2004;8:R443–R450. doi: https://doi.org/10.1186/cc2971</mixed-citation></citation-alternatives></ref><ref id="cit453"><label>453</label><citation-alternatives><mixed-citation xml:lang="ru">Liman TG, Zietemann V, Wiedmann S, et al. Prediction of vascular risk after stroke — protocol and pilot data of the Prospective Cohort with Incident Stroke (PROSCIS). Int J Stroke. 2013;8(6): 484–490. doi: https://doi.org/10.1186/cc2971</mixed-citation><mixed-citation xml:lang="en">Liman TG, Zietemann V, Wiedmann S, et al. Prediction of vascular risk after stroke — protocol and pilot data of the Prospective Cohort with Incident Stroke (PROSCIS). Int J Stroke. 2013;8(6): 484–490. doi: https://doi.org/10.1186/cc2971</mixed-citation></citation-alternatives></ref><ref id="cit454"><label>454</label><citation-alternatives><mixed-citation xml:lang="ru">Mann DM, Kannry JL, Edonyabo D, et al. Rationale, design, and implementation protocol of an electronic health record integrated clinical prediction rule (iCPR) randomized trial in primary care. Implement Sci. 2011;6:109. doi: https://doi.org/10.1186/1748-5908-6-109</mixed-citation><mixed-citation xml:lang="en">Mann DM, Kannry JL, Edonyabo D, et al. Rationale, design, and implementation protocol of an electronic health record integrated clinical prediction rule (iCPR) randomized trial in primary care. Implement Sci. 2011;6:109. doi: https://doi.org/10.1186/1748-5908-6-109</mixed-citation></citation-alternatives></ref><ref id="cit455"><label>455</label><citation-alternatives><mixed-citation xml:lang="ru">Meijs MF, Bots ML, Vonken EJ, et al. Rationale and design of the SMART Heart study: a prediction model for left ventricular hypertrophy in hypertension. Neth Heart J. 2007;15(9):295–298. doi: https://doi.org/10.1007/BF03086003</mixed-citation><mixed-citation xml:lang="en">Meijs MF, Bots ML, Vonken EJ, et al. Rationale and design of the SMART Heart study: a prediction model for left ventricular hypertrophy in hypertension. Neth Heart J. 2007;15(9):295–298. doi: https://doi.org/10.1007/BF03086003</mixed-citation></citation-alternatives></ref><ref id="cit456"><label>456</label><citation-alternatives><mixed-citation xml:lang="ru">Mrdovic I, Savic L, Perunicic J, et al. Development and validation of a risk scoring model to predict net adverse cardiovascular outcomes after primary percutaneous coronary intervention in patients pretreated with 600 mg clopidogrel: rationale and design of the RISK-PCI study. J Interv Cardiol. 2009;22(4):320–328. doi: https://doi.org/10.1111/j.1540-8183.2009.00476.x</mixed-citation><mixed-citation xml:lang="en">Mrdovic I, Savic L, Perunicic J, et al. Development and validation of a risk scoring model to predict net adverse cardiovascular outcomes after primary percutaneous coronary intervention in patients pretreated with 600 mg clopidogrel: rationale and design of the RISK-PCI study. J Interv Cardiol. 2009;22(4):320–328. doi: https://doi.org/10.1111/j.1540-8183.2009.00476.x</mixed-citation></citation-alternatives></ref><ref id="cit457"><label>457</label><citation-alternatives><mixed-citation xml:lang="ru">Nee RJ, Vicenzino B, Jull GA, et al. A novel protocol to develop a prediction model that identifies patients with nerve-related neck and arm pain who benefit from the early introduction of neural tissue management. Contemp Clin Trials. 2011;32(5):760–770. doi: https://doi.org/10.1016/j.cct.2011.05.018</mixed-citation><mixed-citation xml:lang="en">Nee RJ, Vicenzino B, Jull GA, et al. A novel protocol to develop a prediction model that identifies patients with nerve-related neck and arm pain who benefit from the early introduction of neural tissue management. Contemp Clin Trials. 2011;32(5):760–770. doi: https://doi.org/10.1016/j.cct.2011.05.018</mixed-citation></citation-alternatives></ref><ref id="cit458"><label>458</label><citation-alternatives><mixed-citation xml:lang="ru">Pita-Fernández S, Pértega-Diaz S, Valdés-Cañedo F, et al. Incidence of cardiovascular events after kidney transplantation and cardiovascular risk scores: study protocol. BMC Cardiovasc Disord. 2011;11:2. doi: https://doi.org/10.1186/1471-2261-11-2</mixed-citation><mixed-citation xml:lang="en">Pita-Fernández S, Pértega-Diaz S, Valdés-Cañedo F, et al. Incidence of cardiovascular events after kidney transplantation and cardiovascular risk scores: study protocol. BMC Cardiovasc Disord. 2011;11:2. doi: https://doi.org/10.1186/1471-2261-11-2</mixed-citation></citation-alternatives></ref><ref id="cit459"><label>459</label><citation-alternatives><mixed-citation xml:lang="ru">Sanfelix-Genoves J, Peiro S, Sanfelix-Gimeno G, et al. Development and validation of a population-based prediction scale for osteoporotic fracture in the region of Valencia, Spain: the ESOSVAL-R study. BMC Public Health. 2010;10:153. doi: https://doi.org/10.1186/1471-2458-10-153</mixed-citation><mixed-citation xml:lang="en">Sanfelix-Genoves J, Peiro S, Sanfelix-Gimeno G, et al. Development and validation of a population-based prediction scale for osteoporotic fracture in the region of Valencia, Spain: the ESOSVAL-R study. BMC Public Health. 2010;10:153. doi: https://doi.org/10.1186/1471-2458-10-153</mixed-citation></citation-alternatives></ref><ref id="cit460"><label>460</label><citation-alternatives><mixed-citation xml:lang="ru">Siebeling L, terRiet G, van der Wal WM, et al. ICE COLD ERIC — International collaborative effort on chronic obstructive lung disease: exacerbation risk index cohorts — study protocol for an international COPD cohort study. BMC Pulm Med. 2009;9:15. doi: https://doi.org/10.1186/1471-2466-9-15</mixed-citation><mixed-citation xml:lang="en">Siebeling L, terRiet G, van der Wal WM, et al. ICE COLD ERIC — International collaborative effort on chronic obstructive lung disease: exacerbation risk index cohorts — study protocol for an international COPD cohort study. BMC Pulm Med. 2009;9:15. doi: https://doi.org/10.1186/1471-2466-9-15</mixed-citation></citation-alternatives></ref><ref id="cit461"><label>461</label><citation-alternatives><mixed-citation xml:lang="ru">Canadian CT Head and C-Spine (CCC) Study Group. Canadian C-Spine Rule study for alert and stable trauma patients: I. Background and rationale. CJEM. 2002;4(2):84–90.</mixed-citation><mixed-citation xml:lang="en">Canadian CT Head and C-Spine (CCC) Study Group. Canadian C-Spine Rule study for alert and stable trauma patients: I. Background and rationale. CJEM. 2002;4(2):84–90.</mixed-citation></citation-alternatives></ref><ref id="cit462"><label>462</label><citation-alternatives><mixed-citation xml:lang="ru">Canadian CT Head and C-Spine (CCC) Study Group. Canadian C-Spine Rule study for alert and stable trauma patients: II. Study objectives and methodology. CMAJ. 2002;4(3):185–193.</mixed-citation><mixed-citation xml:lang="en">Canadian CT Head and C-Spine (CCC) Study Group. Canadian C-Spine Rule study for alert and stable trauma patients: II. Study objectives and methodology. CMAJ. 2002;4(3):185–193.</mixed-citation></citation-alternatives></ref><ref id="cit463"><label>463</label><citation-alternatives><mixed-citation xml:lang="ru">van Wonderen KE, van der Mark LB, Mohrs J, et al. Prediction and treatment of asthma in preschool children at risk: study design and baseline data of a prospective cohort study in general practice (ARCADE). BMC Pulm Med. 2009;9:13. doi: https://doi.org/10.1186/1471-2466-9-13</mixed-citation><mixed-citation xml:lang="en">van Wonderen KE, van der Mark LB, Mohrs J, et al. Prediction and treatment of asthma in preschool children at risk: study design and baseline data of a prospective cohort study in general practice (ARCADE). BMC Pulm Med. 2009;9:13. doi: https://doi.org/10.1186/1471-2466-9-13</mixed-citation></citation-alternatives></ref><ref id="cit464"><label>464</label><citation-alternatives><mixed-citation xml:lang="ru">Waldron CA, Gallacher J, van der Weijden T, et al. The effect of different cardiovascular risk presentation formats on intentions, understanding and emotional affect: a randomised controlled trial using a web-based risk formatter (protocol). BMC Med Inform Decis Mak. 2010;10:41. doi: https://doi.org/10.1186/1472-6947-10-41</mixed-citation><mixed-citation xml:lang="en">Waldron CA, Gallacher J, van der Weijden T, et al. The effect of different cardiovascular risk presentation formats on intentions, understanding and emotional affect: a randomised controlled trial using a web-based risk formatter (protocol). BMC Med Inform Decis Mak. 2010;10:41. doi: https://doi.org/10.1186/1472-6947-10-41</mixed-citation></citation-alternatives></ref><ref id="cit465"><label>465</label><citation-alternatives><mixed-citation xml:lang="ru">Laine C, Guallar E, Mulrow C, et al. Closing in on the truth about recombinant human bone morphogenetic protein-2: evidence synthesis, data sharing, peer review, and reproducible research. Ann Intern Med. 2013;158(12):916–918. doi: https://doi.org/10.7326/0003-4819-158-12-201306180-00012</mixed-citation><mixed-citation xml:lang="en">Laine C, Guallar E, Mulrow C, et al. Closing in on the truth about recombinant human bone morphogenetic protein-2: evidence synthesis, data sharing, peer review, and reproducible research. Ann Intern Med. 2013;158(12):916–918. doi: https://doi.org/10.7326/0003-4819-158-12-201306180-00012</mixed-citation></citation-alternatives></ref><ref id="cit466"><label>466</label><citation-alternatives><mixed-citation xml:lang="ru">Peng RD. Reproducible research and Biostatistics. Biostatistics. 2009;10(3):405–408. doi: https://doi.org/10.1093/biostatistics/kxp014</mixed-citation><mixed-citation xml:lang="en">Peng RD. Reproducible research and Biostatistics. Biostatistics. 2009;10(3):405–408. doi: https://doi.org/10.1093/biostatistics/kxp014</mixed-citation></citation-alternatives></ref><ref id="cit467"><label>467</label><citation-alternatives><mixed-citation xml:lang="ru">Keiding N. Reproducible research and the substantive context. Biostatistics. 2010;11(3):376–378. doi: https://doi.org/10.1093/biostatistics/kxq033</mixed-citation><mixed-citation xml:lang="en">Keiding N. Reproducible research and the substantive context. Biostatistics. 2010;11(3):376–378. doi: https://doi.org/10.1093/biostatistics/kxq033</mixed-citation></citation-alternatives></ref><ref id="cit468"><label>468</label><citation-alternatives><mixed-citation xml:lang="ru">Vickers AJ. Whose data set is it anyway? Sharing raw data from randomized trials. Trials. 2006;7:15. doi: https://doi.org/10.1186/1745-6215-7-15</mixed-citation><mixed-citation xml:lang="en">Vickers AJ. Whose data set is it anyway? Sharing raw data from randomized trials. Trials. 2006;7:15. doi: https://doi.org/10.1186/1745-6215-7-15</mixed-citation></citation-alternatives></ref><ref id="cit469"><label>469</label><citation-alternatives><mixed-citation xml:lang="ru">Riley RD, Abrams KR, Sutton AJ, et al. Reporting of prognostic markers: current problems and development of guidelines for evidence-based practice in the future. Br J Cancer. 2003;88(8): 1191–1198. doi: https://doi.org/10.1038/sj.bjc.6600886</mixed-citation><mixed-citation xml:lang="en">Riley RD, Abrams KR, Sutton AJ, et al. Reporting of prognostic markers: current problems and development of guidelines for evidence-based practice in the future. Br J Cancer. 2003;88(8): 1191–1198. doi: https://doi.org/10.1038/sj.bjc.6600886</mixed-citation></citation-alternatives></ref><ref id="cit470"><label>470</label><citation-alternatives><mixed-citation xml:lang="ru">Riley RD, Sauerbrei W, Altman DG. Prognostic markers in cancer: the evolution of evidence from single studies to metaanalysis, and beyond. Br J Cancer. 2009;100(8):1219–1229. doi: https://doi.org/10.1038/sj.bjc.6604999</mixed-citation><mixed-citation xml:lang="en">Riley RD, Sauerbrei W, Altman DG. Prognostic markers in cancer: the evolution of evidence from single studies to metaanalysis, and beyond. Br J Cancer. 2009;100(8):1219–1229. doi: https://doi.org/10.1038/sj.bjc.6604999</mixed-citation></citation-alternatives></ref><ref id="cit471"><label>471</label><citation-alternatives><mixed-citation xml:lang="ru">Riley RD, Simmonds MC, Look MP. Evidence synthesis combining individual patient data and aggregate data: a systematic review identified current practice and possible methods. J Clin Epidemiol. 2007;60(5):431–439. doi: https://doi.org/10.1016/j.jclinepi.2006.09.009</mixed-citation><mixed-citation xml:lang="en">Riley RD, Simmonds MC, Look MP. Evidence synthesis combining individual patient data and aggregate data: a systematic review identified current practice and possible methods. J Clin Epidemiol. 2007;60(5):431–439. doi: https://doi.org/10.1016/j.jclinepi.2006.09.009</mixed-citation></citation-alternatives></ref><ref id="cit472"><label>472</label><citation-alternatives><mixed-citation xml:lang="ru">Hemingway H, Riley RD, Altman DG. Ten steps towards improving prognosis research. BMJ. 2009;339:b4184. doi: https://doi.org/10.1136/bmj.b4184</mixed-citation><mixed-citation xml:lang="en">Hemingway H, Riley RD, Altman DG. Ten steps towards improving prognosis research. BMJ. 2009;339:b4184. doi: https://doi.org/10.1136/bmj.b4184</mixed-citation></citation-alternatives></ref><ref id="cit473"><label>473</label><citation-alternatives><mixed-citation xml:lang="ru">Groves T. BMJ policy on data sharing. BMJ. 2010;340:c564. doi: https://doi.org/10.1136/bmj.c564</mixed-citation><mixed-citation xml:lang="en">Groves T. BMJ policy on data sharing. BMJ. 2010;340:c564. doi: https://doi.org/10.1136/bmj.c564</mixed-citation></citation-alternatives></ref><ref id="cit474"><label>474</label><citation-alternatives><mixed-citation xml:lang="ru">Marchionni L, Afsari B, Geman D, Leek JT. A simple and reproducible breast cancer prognostic test. BMC Genomics. 2013; 14:336. doi: https://doi.org/10.1186/1471-2164-14-336</mixed-citation><mixed-citation xml:lang="en">Marchionni L, Afsari B, Geman D, Leek JT. A simple and reproducible breast cancer prognostic test. BMC Genomics. 2013; 14:336. doi: https://doi.org/10.1186/1471-2164-14-336</mixed-citation></citation-alternatives></ref><ref id="cit475"><label>475</label><citation-alternatives><mixed-citation xml:lang="ru">Loder E, Groves T, Macauley D. Registration of observational studies. BMJ. 2010;340:c950. doi: https://doi.org/10.1136/bmj.c950</mixed-citation><mixed-citation xml:lang="en">Loder E, Groves T, Macauley D. Registration of observational studies. BMJ. 2010;340:c950. doi: https://doi.org/10.1136/bmj.c950</mixed-citation></citation-alternatives></ref><ref id="cit476"><label>476</label><citation-alternatives><mixed-citation xml:lang="ru">Chavers S, Fife D, Wacholtz M, et al. Registration of Observational Studies: perspectives from an industry-based epidemiology group. Pharmacoepidemiol Drug Saf. 2011;20(10):1009–1013. doi: https://doi.org/10.1002/pds.2221</mixed-citation><mixed-citation xml:lang="en">Chavers S, Fife D, Wacholtz M, et al. Registration of Observational Studies: perspectives from an industry-based epidemiology group. Pharmacoepidemiol Drug Saf. 2011;20(10):1009–1013. doi: https://doi.org/10.1002/pds.2221</mixed-citation></citation-alternatives></ref><ref id="cit477"><label>477</label><citation-alternatives><mixed-citation xml:lang="ru">Should protocols for observational studies be registered? Lancet. 2010;375(9712):348. doi: https://doi.org/10.1016/S0140-6736(10)60148-1</mixed-citation><mixed-citation xml:lang="en">Should protocols for observational studies be registered? Lancet. 2010;375(9712):348. doi: https://doi.org/10.1016/S0140-6736(10)60148-1</mixed-citation></citation-alternatives></ref><ref id="cit478"><label>478</label><citation-alternatives><mixed-citation xml:lang="ru">Altman DG. The time has come to register diagnostic and prognostic research. Clin Chem. 2014;60(4):580–582. doi: https://doi.org/10.1373/clinchem.2013.220335</mixed-citation><mixed-citation xml:lang="en">Altman DG. The time has come to register diagnostic and prognostic research. Clin Chem. 2014;60(4):580–582. doi: https://doi.org/10.1373/clinchem.2013.220335</mixed-citation></citation-alternatives></ref><ref id="cit479"><label>479</label><citation-alternatives><mixed-citation xml:lang="ru">The registration of observational studies — when metaphors go bad. Epidemiology. 2010;21(5):607–609. doi: https://doi.org/10.1097/EDE.0b013e3181eafbcf</mixed-citation><mixed-citation xml:lang="en">The registration of observational studies — when metaphors go bad. Epidemiology. 2010;21(5):607–609. doi: https://doi.org/10.1097/EDE.0b013e3181eafbcf</mixed-citation></citation-alternatives></ref><ref id="cit480"><label>480</label><citation-alternatives><mixed-citation xml:lang="ru">Sørensen HT, Rothman KJ. The prognosis of research. BMJ. 2010;340:c703. doi: https://doi.org/10.1136/bmj.c703</mixed-citation><mixed-citation xml:lang="en">Sørensen HT, Rothman KJ. The prognosis of research. BMJ. 2010;340:c703. doi: https://doi.org/10.1136/bmj.c703</mixed-citation></citation-alternatives></ref><ref id="cit481"><label>481</label><citation-alternatives><mixed-citation xml:lang="ru">Vandenbroucke JP. Registering observational research: second thoughts. Lancet. 2010;375(9719):982–983. doi: https://doi.org/10.1016/S0140-6736(10)60437-0</mixed-citation><mixed-citation xml:lang="en">Vandenbroucke JP. Registering observational research: second thoughts. Lancet. 2010;375(9719):982–983. doi: https://doi.org/10.1016/S0140-6736(10)60437-0</mixed-citation></citation-alternatives></ref><ref id="cit482"><label>482</label><citation-alternatives><mixed-citation xml:lang="ru">Williams RJ, Tse T, Harlan WR, Zarin DA. Registration of observational studies: Is it time? CMAJ. 2010;182(15):1638–1642. doi: https://doi.org/10.1503/cmaj.092252</mixed-citation><mixed-citation xml:lang="en">Williams RJ, Tse T, Harlan WR, Zarin DA. Registration of observational studies: Is it time? CMAJ. 2010;182(15):1638–1642. doi: https://doi.org/10.1503/cmaj.092252</mixed-citation></citation-alternatives></ref><ref id="cit483"><label>483</label><citation-alternatives><mixed-citation xml:lang="ru">Lenzer J. Majority of panelists on controversial new cholesterol guideline have current or recent ties to drug manufacturers. BMJ. 2013;347:f6989. doi: https://doi.org/10.1136/bmj.f6989</mixed-citation><mixed-citation xml:lang="en">Lenzer J. Majority of panelists on controversial new cholesterol guideline have current or recent ties to drug manufacturers. BMJ. 2013;347:f6989. doi: https://doi.org/10.1136/bmj.f6989</mixed-citation></citation-alternatives></ref><ref id="cit484"><label>484</label><citation-alternatives><mixed-citation xml:lang="ru">Lenzer J, Hoffman JR, Furberg CD, Ioannidis JP. Ensuring the integrity of clinical practice guidelines: a tool for protecting patients. BMJ. 2013;347:f5535. doi: https://doi.org/10.1136/bmj.f5535</mixed-citation><mixed-citation xml:lang="en">Lenzer J, Hoffman JR, Furberg CD, Ioannidis JP. Ensuring the integrity of clinical practice guidelines: a tool for protecting patients. BMJ. 2013;347:f5535. doi: https://doi.org/10.1136/bmj.f5535</mixed-citation></citation-alternatives></ref><ref id="cit485"><label>485</label><citation-alternatives><mixed-citation xml:lang="ru">Simera I. Get the content right: following reporting guidelines will make your research paper more complete, transparent and usable. J Pak Med Assoc. 2013;63(2):283–285.</mixed-citation><mixed-citation xml:lang="en">Simera I. Get the content right: following reporting guidelines will make your research paper more complete, transparent and usable. J Pak Med Assoc. 2013;63(2):283–285.</mixed-citation></citation-alternatives></ref><ref id="cit486"><label>486</label><citation-alternatives><mixed-citation xml:lang="ru">Simera I, Kirtley S, Altman DG. Reporting clinical research: guidance to encourage accurate and transparent research reporting. Maturitas. 2012;72(1):84–87. doi: https://doi.org/10.1016/j.maturitas.2012.02.012</mixed-citation><mixed-citation xml:lang="en">Simera I, Kirtley S, Altman DG. Reporting clinical research: guidance to encourage accurate and transparent research reporting. Maturitas. 2012;72(1):84–87. doi: https://doi.org/10.1016/j.maturitas.2012.02.012</mixed-citation></citation-alternatives></ref><ref id="cit487"><label>487</label><citation-alternatives><mixed-citation xml:lang="ru">Simera I, Moher D, Hirst A, et al. Transparent and accurate reporting increases reliability, utility, and impact of your research: reporting guidelines and the EQUATOR Network. BMC Med. 2010; 8:24. doi: https://doi.org/10.1186/1741-7015-8-24</mixed-citation><mixed-citation xml:lang="en">Simera I, Moher D, Hirst A, et al. Transparent and accurate reporting increases reliability, utility, and impact of your research: reporting guidelines and the EQUATOR Network. BMC Med. 2010; 8:24. doi: https://doi.org/10.1186/1741-7015-8-24</mixed-citation></citation-alternatives></ref><ref id="cit488"><label>488</label><citation-alternatives><mixed-citation xml:lang="ru">Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151:264–269.</mixed-citation><mixed-citation xml:lang="en">Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151:264–269.</mixed-citation></citation-alternatives></ref><ref id="cit489"><label>489</label><citation-alternatives><mixed-citation xml:lang="ru">Little J, Higgins JP, Ioannidis JP, et al. STrengthening the REporting of Genetic Association Studies (STREGA): an extension of the STROBE statement. PLoS Med. 2009;6(2):e22. doi: https://doi.org/10.1371/journal.pmed.1000022</mixed-citation><mixed-citation xml:lang="en">Little J, Higgins JP, Ioannidis JP, et al. STrengthening the REporting of Genetic Association Studies (STREGA): an extension of the STROBE statement. PLoS Med. 2009;6(2):e22. doi: https://doi.org/10.1371/journal.pmed.1000022</mixed-citation></citation-alternatives></ref><ref id="cit490"><label>490</label><citation-alternatives><mixed-citation xml:lang="ru">Kilkenny C, Browne W, Cuthill IC, et al. Animal research: reporting in vivo experiments: the ARRIVE guidelines. J Gene Med. 2010;12(7):561–563. doi: https://doi.org/10.1002/jgm.1473</mixed-citation><mixed-citation xml:lang="en">Kilkenny C, Browne W, Cuthill IC, et al. Animal research: reporting in vivo experiments: the ARRIVE guidelines. J Gene Med. 2010;12(7):561–563. doi: https://doi.org/10.1002/jgm.1473</mixed-citation></citation-alternatives></ref><ref id="cit491"><label>491</label><citation-alternatives><mixed-citation xml:lang="ru">Gagnier JJ, Kienle G, Altman DG, et al. The CARE guidelines: consensus-based clinical case reporting guideline development. J Med Case Rep. 2013;7:223. doi: https://doi.org/10.1186/1752-1947-7-223</mixed-citation><mixed-citation xml:lang="en">Gagnier JJ, Kienle G, Altman DG, et al. The CARE guidelines: consensus-based clinical case reporting guideline development. J Med Case Rep. 2013;7:223. doi: https://doi.org/10.1186/1752-1947-7-223</mixed-citation></citation-alternatives></ref><ref id="cit492"><label>492</label><citation-alternatives><mixed-citation xml:lang="ru">Marshall A, Altman DG, Royston P, Holder RL. Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study. BMC Med Res Methodol. 2010;10:7. doi: https://doi.org/10.1186/1471-2288-10-7</mixed-citation><mixed-citation xml:lang="en">Marshall A, Altman DG, Royston P, Holder RL. Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study. BMC Med Res Methodol. 2010;10:7. doi: https://doi.org/10.1186/1471-2288-10-7</mixed-citation></citation-alternatives></ref><ref id="cit493"><label>493</label><citation-alternatives><mixed-citation xml:lang="ru">Little RJ, Rubin DB. Statistical Analysis With Missing Data. Hoboken, NJ: Wiley; 2002.</mixed-citation><mixed-citation xml:lang="en">Little RJ, Rubin DB. Statistical Analysis With Missing Data. Hoboken, NJ: Wiley; 2002.</mixed-citation></citation-alternatives></ref><ref id="cit494"><label>494</label><citation-alternatives><mixed-citation xml:lang="ru">Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York: J. Wiley &amp; Sons; 1987.</mixed-citation><mixed-citation xml:lang="en">Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York: J. Wiley &amp; Sons; 1987.</mixed-citation></citation-alternatives></ref><ref id="cit495"><label>495</label><citation-alternatives><mixed-citation xml:lang="ru">White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4):377–399. doi: https://doi.org/10.1002/sim.4067</mixed-citation><mixed-citation xml:lang="en">White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4):377–399. doi: https://doi.org/10.1002/sim.4067</mixed-citation></citation-alternatives></ref><ref id="cit496"><label>496</label><citation-alternatives><mixed-citation xml:lang="ru">Harel O, Pellowski J, Kalichman S. Are we missing the importance of missing values in HIV prevention randomized clinical trials? Review and recommendations. AIDS Behav. 2012;16(6): 1382–1393. doi: https://doi.org/10.1007/s10461-011-0125-6</mixed-citation><mixed-citation xml:lang="en">Harel O, Pellowski J, Kalichman S. Are we missing the importance of missing values in HIV prevention randomized clinical trials? Review and recommendations. AIDS Behav. 2012;16(6): 1382–1393. doi: https://doi.org/10.1007/s10461-011-0125-6</mixed-citation></citation-alternatives></ref><ref id="cit497"><label>497</label><citation-alternatives><mixed-citation xml:lang="ru">Schafer JL. Multiple imputation: a primer. Stat Methods Med Res. 1999;8(1):3–15. doi: https://doi.org/10.1177/096228029900800102</mixed-citation><mixed-citation xml:lang="en">Schafer JL. Multiple imputation: a primer. Stat Methods Med Res. 1999;8(1):3–15. doi: https://doi.org/10.1177/096228029900800102</mixed-citation></citation-alternatives></ref><ref id="cit498"><label>498</label><citation-alternatives><mixed-citation xml:lang="ru">Marshall A, Altman DG, Holder RL, Royston P. Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines. BMC Med Res Methodol. 2009;9:57. doi: https://doi.org/10.1186/1471-2288-9-57</mixed-citation><mixed-citation xml:lang="en">Marshall A, Altman DG, Holder RL, Royston P. Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines. BMC Med Res Methodol. 2009;9:57. doi: https://doi.org/10.1186/1471-2288-9-57</mixed-citation></citation-alternatives></ref><ref id="cit499"><label>499</label><citation-alternatives><mixed-citation xml:lang="ru">van Buuren S, Boshuizen HC, Knook DL. Multiple imputation of missing blood pressure covariates in survival analysis. Stat Med. 1999;18(6):681–694. doi: https://doi.org/10.1002/(sici)1097-0258(19990330)18:63.0.co;2-r</mixed-citation><mixed-citation xml:lang="en">van Buuren S, Boshuizen HC, Knook DL. Multiple imputation of missing blood pressure covariates in survival analysis. Stat Med. 1999;18(6):681–694. doi: https://doi.org/10.1002/(sici)1097-0258(19990330)18:63.0.co;2-r</mixed-citation></citation-alternatives></ref><ref id="cit500"><label>500</label><citation-alternatives><mixed-citation xml:lang="ru">Wood AM, White IR, Royston P. How should variable selection be performed with multiply imputed data? Stat Med. 2008;27(17): 3227–3246. doi: https://doi.org/10.1002/sim.3177</mixed-citation><mixed-citation xml:lang="en">Wood AM, White IR, Royston P. How should variable selection be performed with multiply imputed data? Stat Med. 2008;27(17): 3227–3246. doi: https://doi.org/10.1002/sim.3177</mixed-citation></citation-alternatives></ref><ref id="cit501"><label>501</label><citation-alternatives><mixed-citation xml:lang="ru">Turner EL, Dobson JE, Pocock SJ. Categorisation of continuous risk factors in epidemiological publications: a survey of current practice. Epidemiol Perspect Innov. 2010;7:9. doi: https://doi.org/10.1186/1742-5573-7-9</mixed-citation><mixed-citation xml:lang="en">Turner EL, Dobson JE, Pocock SJ. Categorisation of continuous risk factors in epidemiological publications: a survey of current practice. Epidemiol Perspect Innov. 2010;7:9. doi: https://doi.org/10.1186/1742-5573-7-9</mixed-citation></citation-alternatives></ref><ref id="cit502"><label>502</label><citation-alternatives><mixed-citation xml:lang="ru">van Walraven C, Hart RG. Leave ‘em alone — why continuous variables should be analyzed as such. Neuroepidemiology. 2008;30(3):138–139. doi: https://doi.org/10.1159/000126908</mixed-citation><mixed-citation xml:lang="en">van Walraven C, Hart RG. Leave ‘em alone — why continuous variables should be analyzed as such. Neuroepidemiology. 2008;30(3):138–139. doi: https://doi.org/10.1159/000126908</mixed-citation></citation-alternatives></ref><ref id="cit503"><label>503</label><citation-alternatives><mixed-citation xml:lang="ru">Vickers AJ, Lilja H. Cutpoints in clinical chemistry: time for fundamental reassessment. Clin Chem. 2009;55(1):15–17. doi: https://doi.org/10.1373/clinchem.2008.114694</mixed-citation><mixed-citation xml:lang="en">Vickers AJ, Lilja H. Cutpoints in clinical chemistry: time for fundamental reassessment. Clin Chem. 2009;55(1):15–17. doi: https://doi.org/10.1373/clinchem.2008.114694</mixed-citation></citation-alternatives></ref><ref id="cit504"><label>504</label><citation-alternatives><mixed-citation xml:lang="ru">Bennette C, Vickers A. Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents. BMC Med Res Methodol. 2012;12:21. doi: https://doi.org/10.1186/1471-2288-12-21</mixed-citation><mixed-citation xml:lang="en">Bennette C, Vickers A. Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents. BMC Med Res Methodol. 2012;12:21. doi: https://doi.org/10.1186/1471-2288-12-21</mixed-citation></citation-alternatives></ref><ref id="cit505"><label>505</label><citation-alternatives><mixed-citation xml:lang="ru">Dawson NV, Weiss R. Dichotomizing continuous variables in statistical analysis: a practice to avoid. Med Decis Making. 2012; 32(2):225–226. doi: https://doi.org/10.1177/0272989X12437605</mixed-citation><mixed-citation xml:lang="en">Dawson NV, Weiss R. Dichotomizing continuous variables in statistical analysis: a practice to avoid. Med Decis Making. 2012; 32(2):225–226. doi: https://doi.org/10.1177/0272989X12437605</mixed-citation></citation-alternatives></ref><ref id="cit506"><label>506</label><citation-alternatives><mixed-citation xml:lang="ru">Royston P, Altman DG. Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling. Appl Stat. 1994;43(3):429–467. doi: https://doi.org/10.2307/2986270</mixed-citation><mixed-citation xml:lang="en">Royston P, Altman DG. Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling. Appl Stat. 1994;43(3):429–467. doi: https://doi.org/10.2307/2986270</mixed-citation></citation-alternatives></ref><ref id="cit507"><label>507</label><citation-alternatives><mixed-citation xml:lang="ru">Harrell FE, Lee KL, Pollock BG. Regression models in clinical studies: determining relationships between predictors and response. J Natl Cancer Inst. 1988;80(15):1198–1202. doi: https://doi.org/10.1093/jnci/80.15.1198</mixed-citation><mixed-citation xml:lang="en">Harrell FE, Lee KL, Pollock BG. Regression models in clinical studies: determining relationships between predictors and response. J Natl Cancer Inst. 1988;80(15):1198–1202. doi: https://doi.org/10.1093/jnci/80.15.1198</mixed-citation></citation-alternatives></ref><ref id="cit508"><label>508</label><citation-alternatives><mixed-citation xml:lang="ru">Schumacher M, Binder H, Gerds T. Assessment of survival prediction models based on microarray data. Bioinformatics. 2007;23(14): 1768–1774. doi: https://doi.org/10.1093/bioinformatics/btm232</mixed-citation><mixed-citation xml:lang="en">Schumacher M, Binder H, Gerds T. Assessment of survival prediction models based on microarray data. Bioinformatics. 2007;23(14): 1768–1774. doi: https://doi.org/10.1093/bioinformatics/btm232</mixed-citation></citation-alternatives></ref><ref id="cit509"><label>509</label><citation-alternatives><mixed-citation xml:lang="ru">Subramanian J, Simon R. Gene expression-based prognostic signatures in lung cancer: ready for clinical use? J Natl Cancer Inst. 2010;102(7):464–474. doi: https://doi.org/10.1093/jnci/djq025</mixed-citation><mixed-citation xml:lang="en">Subramanian J, Simon R. Gene expression-based prognostic signatures in lung cancer: ready for clinical use? J Natl Cancer Inst. 2010;102(7):464–474. doi: https://doi.org/10.1093/jnci/djq025</mixed-citation></citation-alternatives></ref><ref id="cit510"><label>510</label><citation-alternatives><mixed-citation xml:lang="ru">Dupuy A, Simon RM. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst. 2007;99(2):147–157. doi: https://doi.org/10.1093/jnci/djk018</mixed-citation><mixed-citation xml:lang="en">Dupuy A, Simon RM. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst. 2007;99(2):147–157. doi: https://doi.org/10.1093/jnci/djk018</mixed-citation></citation-alternatives></ref><ref id="cit511"><label>511</label><citation-alternatives><mixed-citation xml:lang="ru">Boulesteix AL. Validation in bioinformatics and molecular medicine. Brief Bioinform. 2011;12(3):187–188. doi: https://doi.org/10.1093/bib/bbr027</mixed-citation><mixed-citation xml:lang="en">Boulesteix AL. Validation in bioinformatics and molecular medicine. Brief Bioinform. 2011;12(3):187–188. doi: https://doi.org/10.1093/bib/bbr027</mixed-citation></citation-alternatives></ref><ref id="cit512"><label>512</label><citation-alternatives><mixed-citation xml:lang="ru">Jelizarow M, Guillemot V, Tenenhaus A, et al. Over-optimism in bioinformatics: an illustration. Bioinformatics. 2010;26(16): 1990–1998. doi: https://doi.org/10.1093/bioinformatics/btq323</mixed-citation><mixed-citation xml:lang="en">Jelizarow M, Guillemot V, Tenenhaus A, et al. Over-optimism in bioinformatics: an illustration. Bioinformatics. 2010;26(16): 1990–1998. doi: https://doi.org/10.1093/bioinformatics/btq323</mixed-citation></citation-alternatives></ref><ref id="cit513"><label>513</label><citation-alternatives><mixed-citation xml:lang="ru">Vickers AJ, Cronin AM. Everything you always wanted to know about evaluating prediction models (but were too afraid to ask). Urology. 2010;76(6):1298–1301. doi: https://doi.org/10.1016/j.urology.2010.06.019</mixed-citation><mixed-citation xml:lang="en">Vickers AJ, Cronin AM. Everything you always wanted to know about evaluating prediction models (but were too afraid to ask). Urology. 2010;76(6):1298–1301. doi: https://doi.org/10.1016/j.urology.2010.06.019</mixed-citation></citation-alternatives></ref><ref id="cit514"><label>514</label><citation-alternatives><mixed-citation xml:lang="ru">Austin PC, Steyerberg EW. Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers. Stat Med. 2014;33(3):517–535. doi: https://doi.org/10.1002/sim.5941</mixed-citation><mixed-citation xml:lang="en">Austin PC, Steyerberg EW. Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers. Stat Med. 2014;33(3):517–535. doi: https://doi.org/10.1002/sim.5941</mixed-citation></citation-alternatives></ref><ref id="cit515"><label>515</label><citation-alternatives><mixed-citation xml:lang="ru">Crowson CS, Atkinson EJ, Therneau TM. Assessing calibration of prognostic risk scores. Stat Methods Med Res. 2016;25(4): 1692–1706. doi: https://doi.org/10.1177/0962280213497434</mixed-citation><mixed-citation xml:lang="en">Crowson CS, Atkinson EJ, Therneau TM. Assessing calibration of prognostic risk scores. Stat Methods Med Res. 2016;25(4): 1692–1706. doi: https://doi.org/10.1177/0962280213497434</mixed-citation></citation-alternatives></ref><ref id="cit516"><label>516</label><citation-alternatives><mixed-citation xml:lang="ru">Vach W. Calibration of clinical prediction rules does not just assess bias. J Clin Epidemiol. 2013;66(11):1296–1301. doi: https://doi.org/10.1016/j.jclinepi.2013.06.003</mixed-citation><mixed-citation xml:lang="en">Vach W. Calibration of clinical prediction rules does not just assess bias. J Clin Epidemiol. 2013;66(11):1296–1301. doi: https://doi.org/10.1016/j.jclinepi.2013.06.003</mixed-citation></citation-alternatives></ref><ref id="cit517"><label>517</label><citation-alternatives><mixed-citation xml:lang="ru">Miller ME, Hui SL, Tierney WM. Validation techniques for logistic-regression models. Stat Med. 1991;10(8):1213–1226. doi: https://doi.org/10.1002/sim.4780100805</mixed-citation><mixed-citation xml:lang="en">Miller ME, Hui SL, Tierney WM. Validation techniques for logistic-regression models. Stat Med. 1991;10(8):1213–1226. doi: https://doi.org/10.1002/sim.4780100805</mixed-citation></citation-alternatives></ref><ref id="cit518"><label>518</label><citation-alternatives><mixed-citation xml:lang="ru">Cox DR. Two further applications of a model for binary regression. Biometrika. 1958;45:562–565.</mixed-citation><mixed-citation xml:lang="en">Cox DR. Two further applications of a model for binary regression. Biometrika. 1958;45:562–565.</mixed-citation></citation-alternatives></ref><ref id="cit519"><label>519</label><citation-alternatives><mixed-citation xml:lang="ru">D’Agostino RB, Nam BH. Evaluation of the performance of survival analysis models: discrimination and calibration measures. In: Handbook of Statistics, Survival Methods. Balakrishnan N, Rao CR, eds. Amsterdam: Elsevier; 2004. pp. 1–25.</mixed-citation><mixed-citation xml:lang="en">D’Agostino RB, Nam BH. Evaluation of the performance of survival analysis models: discrimination and calibration measures. In: Handbook of Statistics, Survival Methods. Balakrishnan N, Rao CR, eds. Amsterdam: Elsevier; 2004. pp. 1–25.</mixed-citation></citation-alternatives></ref><ref id="cit520"><label>520</label><citation-alternatives><mixed-citation xml:lang="ru">Grønnesby JK, Borgan O. A method for checking regression models in survival analysis based on the risk score. Lifetime Data Anal. 1996;2(4):315–328. doi: https://doi.org/10.1007/BF00127305</mixed-citation><mixed-citation xml:lang="en">Grønnesby JK, Borgan O. A method for checking regression models in survival analysis based on the risk score. Lifetime Data Anal. 1996;2(4):315–328. doi: https://doi.org/10.1007/BF00127305</mixed-citation></citation-alternatives></ref><ref id="cit521"><label>521</label><citation-alternatives><mixed-citation xml:lang="ru">Bertolini G, D’Amico R, Nardi D, et al. One model, several results: the paradox of the Hosmer-Lemeshow goodness-of-fit test for the logistic regression model. J Epidemiol Biostat. 2000;5(4):251–253.</mixed-citation><mixed-citation xml:lang="en">Bertolini G, D’Amico R, Nardi D, et al. One model, several results: the paradox of the Hosmer-Lemeshow goodness-of-fit test for the logistic regression model. J Epidemiol Biostat. 2000;5(4):251–253.</mixed-citation></citation-alternatives></ref><ref id="cit522"><label>522</label><citation-alternatives><mixed-citation xml:lang="ru">Kramer AA, Zimmerman JE. Assessing the calibration of mortality benchmarks in critical care: the Hosmer-Lemeshow test revisited. Crit Care Med. 2007;35(9):2052–2056. doi: https://doi.org/10.1097/01.CCM.0000275267.64078.B0</mixed-citation><mixed-citation xml:lang="en">Kramer AA, Zimmerman JE. Assessing the calibration of mortality benchmarks in critical care: the Hosmer-Lemeshow test revisited. Crit Care Med. 2007;35(9):2052–2056. doi: https://doi.org/10.1097/01.CCM.0000275267.64078.B0</mixed-citation></citation-alternatives></ref><ref id="cit523"><label>523</label><citation-alternatives><mixed-citation xml:lang="ru">Marcin JP, Romano PS. Size matters to a model’s fit. Crit Care Med. 2007;35(9):2212–2213. doi: https://doi.org/10.1097/01.CCM.0000281522.70992.EF</mixed-citation><mixed-citation xml:lang="en">Marcin JP, Romano PS. Size matters to a model’s fit. Crit Care Med. 2007;35(9):2212–2213. doi: https://doi.org/10.1097/01.CCM.0000281522.70992.EF</mixed-citation></citation-alternatives></ref><ref id="cit524"><label>524</label><citation-alternatives><mixed-citation xml:lang="ru">Bannister CA, Poole CD, Jenkins-Jones S, et al. External validation of the UKPDS risk engine in incident type 2 diabetes: a need for new type 2 diabetes-specific risk equations. Diab Care. 2014;37(2):537–545. doi: https://doi.org/10.2337/dc13-1159</mixed-citation><mixed-citation xml:lang="en">Bannister CA, Poole CD, Jenkins-Jones S, et al. External validation of the UKPDS risk engine in incident type 2 diabetes: a need for new type 2 diabetes-specific risk equations. Diab Care. 2014;37(2):537–545. doi: https://doi.org/10.2337/dc13-1159</mixed-citation></citation-alternatives></ref><ref id="cit525"><label>525</label><citation-alternatives><mixed-citation xml:lang="ru">Van Hoorde K, Vergouwe Y, Timmerman D, et al. Assessing calibration of multinomial risk prediction models. Stat Med. 2014; 33(15):2585–2596. doi: https://doi.org/10.1002/sim.6114</mixed-citation><mixed-citation xml:lang="en">Van Hoorde K, Vergouwe Y, Timmerman D, et al. Assessing calibration of multinomial risk prediction models. Stat Med. 2014; 33(15):2585–2596. doi: https://doi.org/10.1002/sim.6114</mixed-citation></citation-alternatives></ref><ref id="cit526"><label>526</label><citation-alternatives><mixed-citation xml:lang="ru">Cook NR. Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve. Clin Chem. 2008;54(1):17–23. doi: https://doi.org/10.1373/clinchem.2007.096529</mixed-citation><mixed-citation xml:lang="en">Cook NR. Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve. Clin Chem. 2008;54(1):17–23. doi: https://doi.org/10.1373/clinchem.2007.096529</mixed-citation></citation-alternatives></ref><ref id="cit527"><label>527</label><citation-alternatives><mixed-citation xml:lang="ru">Pencina MJ, D’Agostino RB, Song L. Quantifying discrimination of Framingham risk functions with different survival C statistics. Stat Med. 2012;31(15):1543–1553. doi: https://doi.org/10.1002/sim.4508</mixed-citation><mixed-citation xml:lang="en">Pencina MJ, D’Agostino RB, Song L. Quantifying discrimination of Framingham risk functions with different survival C statistics. Stat Med. 2012;31(15):1543–1553. doi: https://doi.org/10.1002/sim.4508</mixed-citation></citation-alternatives></ref><ref id="cit528"><label>528</label><citation-alternatives><mixed-citation xml:lang="ru">Van Calster B, Van Belle V, Vergouwe Y, et al. Extending the c-statistic to nominal polytomous outcomes: the polytomous discrimination index. Stat Med. 2012;31(23):2610–2626. doi: https://doi.org/10.1002/sim.532</mixed-citation><mixed-citation xml:lang="en">Van Calster B, Van Belle V, Vergouwe Y, et al. Extending the c-statistic to nominal polytomous outcomes: the polytomous discrimination index. Stat Med. 2012;31(23):2610–2626. doi: https://doi.org/10.1002/sim.532</mixed-citation></citation-alternatives></ref><ref id="cit529"><label>529</label><citation-alternatives><mixed-citation xml:lang="ru">Wolbers M, Blanche P, Koller MT, et al. Concordance for prognostic models with competing risks. Biostatistics. 2014;15(3): 526–539. doi: https://doi.org/10.1093/biostatistics/kxt059</mixed-citation><mixed-citation xml:lang="en">Wolbers M, Blanche P, Koller MT, et al. Concordance for prognostic models with competing risks. Biostatistics. 2014;15(3): 526–539. doi: https://doi.org/10.1093/biostatistics/kxt059</mixed-citation></citation-alternatives></ref><ref id="cit530"><label>530</label><citation-alternatives><mixed-citation xml:lang="ru">Pencina MJ, D’Agostino RB, Demler OV. Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models. Stat Med. 2012;31(2):101–113. doi: https://doi.org/10.1002/sim.4348</mixed-citation><mixed-citation xml:lang="en">Pencina MJ, D’Agostino RB, Demler OV. Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models. Stat Med. 2012;31(2):101–113. doi: https://doi.org/10.1002/sim.4348</mixed-citation></citation-alternatives></ref><ref id="cit531"><label>531</label><citation-alternatives><mixed-citation xml:lang="ru">Bradburn MJ, Clark TG, Love SB, Altman DG. Survival analysis part III: multivariate data analysis — choosing a model and assessing its adequacy and fit. Br J Cancer. 2003;89(4):605–611. doi: https://doi.org/10.1038/sj.bjc.6601120</mixed-citation><mixed-citation xml:lang="en">Bradburn MJ, Clark TG, Love SB, Altman DG. Survival analysis part III: multivariate data analysis — choosing a model and assessing its adequacy and fit. Br J Cancer. 2003;89(4):605–611. doi: https://doi.org/10.1038/sj.bjc.6601120</mixed-citation></citation-alternatives></ref><ref id="cit532"><label>532</label><citation-alternatives><mixed-citation xml:lang="ru">Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for the systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med. 2014;11(10): e1001744. doi: https://doi.org/10.1371/journal.pmed.1001744</mixed-citation><mixed-citation xml:lang="en">Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for the systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med. 2014;11(10): e1001744. doi: https://doi.org/10.1371/journal.pmed.1001744</mixed-citation></citation-alternatives></ref><ref id="cit533"><label>533</label><citation-alternatives><mixed-citation xml:lang="ru">Moons K.G.M., Altman D.G., Reitsma J.B. и др. Прозрачная отчетность о многофакторной предсказательной модели для индивидуального прогнозирования или диагностики (TRIPOD): разъяснения и уточнения // Digital Diagnostics. — 2022. — Т. 3. — № 3. — C. 232–322. — doi: https://doi.org/10.17816/DD110794</mixed-citation><mixed-citation xml:lang="en">Moons KGM, Altman DG, Reitsma JB, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Translation in to Russian. Digital Diagnostics. 2022;3(3):232–322. doi: https://doi.org/10.17816/DD110794</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
