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Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis ( TRIPOD ): the TRIPOD Statement
Oleh:
Collins, G.S.
;
Reitsma, J.B.
;
Altman, D. G.
;
Moons, K. G. M.
Jenis:
Article from Article - diterbitkan di jurnal ilmiah internasional
Dalam koleksi:
BJS: British Journal of Surgery vol. 102 no. 03 (Feb. 2015)
,
page 148-158.
Topik:
TRIPOD
;
prediction model
;
diagnostic model
;
prognostic model
Fulltext:
bjs9736.pdf
(243.88KB)
Ketersediaan
Perpustakaan FK
Nomor Panggil:
B15.K
Non-tandon:
1 (dapat dipinjam: 0)
Tandon:
tidak ada
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Isi artikel
Background 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. Methods 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. Results The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. Conclusion The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used.
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