Article

CHARMS and PROBAST+AI: an updated template for Data Extraction and Risk of Bias Assessment in systematic reviews of prediction models

2026-08-31

Abstract excerpt

<h4>Background: </h4> Systematic reviews of clinical prediction models increasingly include studies using artificial intelligence (AI) and machine learning (ML) methods alongside traditional multivariable regression approaches. A previously published Excel tool enabled standardised data extraction using the CHARMS checklist and risk of bias assessment using PROBAST. The recent publication of the PROBAST+AI framewo...

Identifiers and source

Literature Corpus work: 82a226f0-64a2-5b74-8343-3752a9e114ba
DOI: 10.64898/2026.08.26.26361189

Open publication

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Click a neighbor to travelStep 1 · 12 closest

CHARMS and PROBAST+AI: an updated template for Data Extraction and Risk of Bias Assessment in systematic reviews of prediction modelsDOI 10.64898/2026.08.26.26361189Read

Clinical Prediction Models: Foundational ConceptsSemantic proximity Read

Empirical Sample Size Determination for Popular Classification Algorithms in Clinical ResearchSemantic proximity Read

Stage-wise algorithmic bias, its reporting, and relation to classical systematic review biases in AI-based automated screening in health sciences: A structured literature reviewSemantic proximity Read

A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health dataSemantic proximity Read

Approaches in Analyzing Predictors of Trial Failure: A Scoping Review and Meta-epidemiological studySemantic proximity Read

JARVIS, should this study be selected for full-text screening? Performance of a Joint AI-ReViewer Interactive Screening tool for systematic reviewsSemantic proximity Read

Machine learning for identifying relevant publications in updates of systematic reviews of diagnostic test studiesSemantic proximity Read

Artificial Intelligence for Cardiac Biomarkers After Myocardial Infarction: A Systematic Review and a Leakage-Aware Modeling FrameworkSemantic proximity Read

Considerations in the Reliability and Fairness Audits of Predictive Models for Advance Care PlanningSemantic proximity Read

Multivariable Prediction Models for Health Care Spending Using Machine Learning: a Protocol of a Systematic ReviewSemantic proximity Read

Leveraging Machine Learning Models to Predict Probability of Technical and Regulatory SuccessSemantic proximity Read

The Comparability of Manual vs. Algorithm-Based Calculation of Clinical Trial Methodological Quality IndicesSemantic proximity Read