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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".