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Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration. Translation in to Russian

Authors :
Karel G.M. Moons
Douglas G. Altman
Johannes B. Reitsma
John P.A. Loannidis
Petra Macaskill
Ewout W. Steyerberg
Andrew J. Vickers
David F. Ransohoff
Gary S. Collins
Source :
Digital Diagnostics, Vol 3, Iss 3, Pp 232-322 (2022)
Publication Year :
2022
Publisher :
Eco-Vector, 2022.

Abstract

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. For members of the TRIPOD Group, see the Appendix. This article is the translation in to Russian by Dr. Ruslan Saygitov (ORCID: 0000-0002-8915-6153) from the original published in [Ann Intern Med. 2015; 162:W1-W73. doi: 10.7326/M14-0698 ].

Details

Language :
English, Russian, Chinese
ISSN :
27128490 and 27128962
Volume :
3
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Digital Diagnostics
Publication Type :
Academic Journal
Accession number :
edsdoj.68fbe2290bb4250aca8f24f847e87ae
Document Type :
article
Full Text :
https://doi.org/10.17816/DD110794