All in the family: a referral strategy for screening relatives of individuals with premature coronary artery disease
Bibliographic record
Abstract
This editorial refers to ‘A family-based strategy to identify and prevent premature cardiovascular disease: a feasibility pilot study’, by P. Charleux et al., https://doi.org/10.1093/eurjpc/zwaf076. A family history of premature cardiovascular disease (CVD) is a strong risk factor for cardiac events. For first-degree relatives of people with premature CV disease, this risk may involve more than just genetics; it may include similar environmental exposures, lifestyle factors, and social determinants of health. Early detection of CV risk factors and disease is important for timely intervention and prevention of adverse cardiac events in relatives of people with premature CV disease. While CV professional society guidelines recommend screening individuals with a family history of premature CV disease,1 there is little practical guidance on how to identify these individuals in a systematic manner. Clinicians require better guidance on upstream measures to identify and manage at-risk, first-degree relatives of patients with premature CVD. Identifying and referring first-degree relatives at the time of the proband’s index hospitalization is an approach that attempts to capitalize on the motivation of the proband and their relatives to take control of their CV health at a moment of crisis. Prior research has found that individuals hospitalized for an acute coronary event are highly motivated to refer their relatives for CV screening.2 Just as importantly, their relatives are highly willing to participate in screening programmes when directly contacted by the CV proband during and immediately after index hospitalization.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".