TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
- Teacher spread
- 0.040 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- BMJ
- Topic
- Artificial Intelligence in Healthcare and Education
- Field
- Medicine
- Canadian institutions
- Vector InstituteHospital for Sick ChildrenPrincess Margaret Cancer CentreSickKids FoundationUniversity of TorontoUniversity Health Network
- Funders
- Health Data Research UKNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteEngineering and Physical Sciences Research CouncilUniversity of OxfordUniversity of WarwickVlaamse regeringUniversity of Cape TownMedizinische Universität WienUniversitair Medisch Centrum UtrechtUniversität WienMassachusetts Institute of TechnologyEuropean CommissionUniversity of TorontoWellcome TrustCancer Research UKUniversity College LondonImperial College LondonHospital for Sick ChildrenNorthwestern UniversityFeinberg School of MedicineKU LeuvenNederlandse Organisatie voor Wetenschappelijk OnderzoekUniversity of East AngliaMedical Research CouncilDepartment of Health and Social CareNational Institute for Health and Care ResearchUK Research and InnovationNational Institute for Health and Care Excellence
- Keywords
- Tripod (photography)ChecklistMachine learningComputer scienceArtificial intelligenceEngineeringPsychology
- Has abstract in OpenAlex
- yes