BMJ· 2024Q1
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
- 2,422citations
- Q1SCImago
- 2024year
Short summary
The updated TRIPOD+AI statement provides harmonized guidance for reporting studies that develop or evaluate clinical prediction models, now encompassing both traditional regression and modern machine learning/AI methods.
AI-generated from the title and abstract; the full text is not read.
Key points
- TRIPOD+AI is a new standard for reporting clinical prediction models.
- It unifies reporting guidelines for both regression and machine learning/AI methods.
- A new 27-item checklist and an abstract checklist are provided.
- The goal is to enhance transparency, completeness, and accuracy in reporting.
AI-generated from the title and abstract; the full text is not read.
Abstract
The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting recommendations for studies developing or evaluating the performance of a prediction model. Methodological advances in the field of prediction have since included the widespread use of artificial intelligence (AI) powered by machine learning methods to develop prediction models. An update to the TRIPOD statement is thus needed. TRIPOD+AI provides harmonised guidance for reporting prediction model studies, irrespective of whether regression modelling or machine learning methods have been used. The new checklist supersedes the TRIPOD 2015 checklist, which should no longer be used. This article describes the development of TRIPOD+AI and presents the expanded 27 item checklist with more detailed explanation of each reporting recommendation, and the TRIPOD+AI for Abstracts checklist. TRIPOD+AI aims to promote the complete, accurate, and transparent reporting of studies that develop a prediction model or evaluate its performance. Complete reporting will facilitate study appraisal, model evaluation, and model implementation.
The authors' abstract, as published at the source. BMJ, 2024 · DOI ↗
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Field: Health Informatics
Health InformaticsMedicine