Literature DB >> 25600898

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.

G S Collins1, J B Reitsma, D G Altman, K G M Moons.   

Abstract

Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study, regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
© 2015 Joint copyright. The Authors and Annals of Internal Medicine. Diabetic Medicine published by John Wiley Ltd. on behalf of Diabetes UK.

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Year:  2015        PMID: 25600898     DOI: 10.1111/dme.12654

Source DB:  PubMed          Journal:  Diabet Med        ISSN: 0742-3071            Impact factor:   4.359


  6 in total

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Journal:  Quant Imaging Med Surg       Date:  2022-02

2.  Early warning score validation methodologies and performance metrics: a systematic review.

Authors:  Andrew Hao Sen Fang; Wan Tin Lim; Tharmmambal Balakrishnan
Journal:  BMC Med Inform Decis Mak       Date:  2020-06-18       Impact factor: 2.796

Review 3.  FINDRISC in Latin America: a systematic review of diagnosis and prognosis models.

Authors:  Diego J Aparcana-Granda; Jhonatan R Mejia; Rodrigo M Carrillo-Larco; Antonio Bernabé-Ortiz
Journal:  BMJ Open Diabetes Res Care       Date:  2020-04

4.  Risk scores for type 2 diabetes mellitus in Latin America: a systematic review of population-based studies.

Authors:  R M Carrillo-Larco; D J Aparcana-Granda; J R Mejia; N C Barengo; A Bernabe-Ortiz
Journal:  Diabet Med       Date:  2019-09-06       Impact factor: 4.359

5.  Modified paediatric preoperative risk prediction score to predict postoperative ICU admission in children: a retrospective cohort study.

Authors:  Chunwei Lian; Pei Wang; Qingxia Fu; Xudong Du; Junzheng Wu; Qingquan Lian; Wangning ShangGuan
Journal:  BMJ Open       Date:  2020-03-18       Impact factor: 2.692

6.  Development and internal validation of a Nomogram for preoperative prediction of surgical treatment effect on cesarean section diverticulum.

Authors:  Yizhi Wang; Qinyi Zhu; Feikai Lin; Li Xie; Jiarui Li; Xipeng Wang
Journal:  BMC Womens Health       Date:  2019-11-11       Impact factor: 2.809

  6 in total

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