Machine learning in the prediction of treatment response in rheumatoid arthritis: A systematic review
Authors' conclusion
Our critical appraisal revealed unclear and high risk of bias in most of the identified models
Study context
- Population
- Cohort studies deriving and/or validating machine learning (ML) models predicting rheumatoid arthritis (RA) treatment response
- Participants
- 29 eligible studies
- Outcome
- Mean adherence to TRIPOD guidelines
Methodological detailsPRISMA, CHARMS checklist, TRIPOD and PROBAST appraisal, 210 unduplicated records identified; 29 eligible studies retained
- PRISMA
- CHARMS checklist
- TRIPOD and PROBAST appraisal
- 210 unduplicated records identified; 29 eligible studies retained
Pooled result
As reported in the abstract
Further pooled results
Risk of bias (PROBAST)
%
- Unclear risk of bias
- 41.4
- High risk of bias
- 37.9
Parts of this Brief are withheld: the automatic checks could not confirm them against the abstract.
Original publication
Machine learning in the prediction of treatment response in rheumatoid arthritis: A systematic review. Semin Arthritis Rheum. Published 23 July 2024. doi:10.
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