Screening families of patients with premature coronary heart disease to identify avoidable cardiovascular risk: a cross-sectional study of family members and a general population comparison group
Bibliographic record
Abstract
BACKGROUND: Primary prevention should be targeted at individuals with high global cardiovascular risk, but research is lacking on how best to identify such individuals in the general population. Family history is a good proxy measure of global risk and may provide an efficient mechanism for identifying high risk individuals. The aim was to test the feasibility of using patients with premature cardiovascular disease to recruit family members as a means of identifying and screening high-risk individuals. FINDINGS: We recruited family members of 50 patients attending a cardiology clinic for premature coronary heart disease (CHD). We compared their cardiovascular risk with a general population control group, and determined their perception of their risk and current level of screening. 103 (36%) family members attended screening (27 siblings, 48 adult offspring and 28 partners). Five (5%) had prevalent CHD. A significantly higher percentage had an ASSIGN risk score >20% compared with the general population (13% versus 2%, p < 0.001). Only 37% of family members were aware they were at increased risk and only 50% had had their blood pressure and serum cholesterol level checked in the previous three years. CONCLUSIONS: Patients attending hospital for premature CHD provide a mechanism to contact family members and this can identify individuals with a high global risk who are not currently screened.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".