Incorporating behavioural and psychological factors into cardiovascular disease risk prediction models: protocol for a systematic review
Bibliographic record
Abstract
OBJECTIVES: This systematic review aims to: (1) evaluate how behavioural and psychological factors have been incorporated into cardiovascular disease (CVD) risk prediction models; (2) assess their impact on model performance metrics such as area under the curve (AUC) and net reclassification index (NRI); and (3) identify which specific variables are most consistently associated with predictive improvements. This protocol is reported in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocols (PRISMA-P) 2015, and the systematic review will follow the Cochrane Handbook and report findings based on PRISMA 2020. DESIGN: A systematic review protocol developed in accordance with the (PRISMA-P) 2015 guidelines. DATA SOURCES: Systematic searches will be carried out in PubMed, Scopus, Web of Science and Google Scholar, limited to studies published from 2019 to 2024. ELIGIBILITY CRITERIA: Peer-reviewed original studies involving adult populations (≥18 years) at risk of CVD, incorporating at least one behavioural or psychological variable into a CVD risk prediction model. Studies must report model performance metrics such as AUC or NRI. Studies focusing solely on biochemical or demographic factors, paediatric populations, or non-CVD outcomes will be excluded. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers will screen eligible studies, extract data and assess study quality using the Newcastle-Ottawa Scale and Quality in Prognostic Studies tool. A narrative synthesis will be performed, with meta-analysis conducted if feasible. ETHICS AND DISSEMINATION: Ethical approval is not required for this study. Findings will be disseminated through peer-reviewed publication and conference presentations. PROSPERO REGISTRATION NUMBER: CRD420251014218.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.080 | 0.129 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.020 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.101 | 0.013 |
Machine scores (provisional)
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.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".