Social bias in artificial intelligence algorithms designed to improve cardiovascular risk assessment relative to the Framingham Risk Score: a protocol for a systematic review
Bibliographic record
Abstract
INTRODUCTION: Cardiovascular disease (CVD) prevention relies on timely identification of and intervention for individuals at risk. Risk assessment models such as the Framingham Risk Score (FRS) have been shown to over-estimate or under-estimate risk in certain groups, such as socioeconomically disadvantaged populations. Artificial intelligence (AI) and machine learning (ML) could be used to address such equity gaps to improve risk assessment; however, critical appraisal is warranted before ML-informed clinical decision-making is implemented. METHODS AND ANALYSIS: This study will employ an equity-lens to identify sources of bias (ie, race/ethnicity, gender and social stratum) in ML algorithms designed to improve CVD risk assessment relative to the FRS. A comprehensive literature search will be completed using MEDLINE, Embase and IEEE to answer the research question: do AI algorithms that are designed for the estimation of CVD risk and that compare performance with the FRS address the sources of bias inherent in the FRS? No study date filters will be imposed on the search, but English language filters will be applied. Studies describing a specific algorithm or ML approach that provided a risk assessment output for coronary artery disease, heart failure, cardiac arrhythmias (ie, atrial fibrillation), stroke or a global CVD risk score, and that compared performance with the FRS are eligible for inclusion. Papers describing algorithms for the diagnosis rather than the prevention of CVD will be excluded. A structured narrative review analysis of included studies will be completed. ETHICS AND DISSEMINATION: Ethics approval was not required. Ethics exemption was formally received from the General Research Ethics Board at Queen's University. The completed systematic review will be submitted to a peer-reviewed journal and parts of the work will be presented at relevant conferences.
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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 | 0.104 | 0.150 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.022 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.063 | 0.010 |
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".