A Patient-Led Referral Strategy for Cardiovascular Screening of Family and Household Members at the Time of Cardiac Intensive Care Unit Admission
Bibliographic record
Abstract
Background Screening relatives of patients with ischemic heart disease can identify over half of the population with poorly controlled cardiovascular (CV) risk factors. Family or household members (FMs) may be highly motivated to undergo CV primary prevention screening at the time of their relative's admission to the Cardiovascular Intensive Care Unit (CICU). Methods Patients aged ≤ 70 years admitted to a tertiary CICU for an acute coronary event were given a letter to refer FMs for CV screening. Interested FMs underwent CV risk-factor assessment and primary prevention counselling. The objectives were to identify FMs with an intermediate or high modified 10-year Framingham risk score (FRS) and to evaluate whether a family-oriented primary prevention strategy improved CV risk. Results There were 51 CV probands who referred 101 FMs (62 family, 39 household; mean age: 44.8 ± 15.3; 65 (64.4%) female) for screening. One-third of FMs aged ≥ 30 years (n = 28 of 84; 32.1%) had a new diagnosis of either hypertension, diabetes, or dyslipidemia. Nearly half of FMs (n = 38; 45.2%) had an intermediate or high modified Framingham 10-year CV risk. In FMs aged ≥ 30 years attending the 6-month follow-up (51 of 84; 60.7%), the mean FRS decreased by 4.6% (from 13.2% ± 12.7 to 8.6% ± 10.0, P < 0.001), and 30.4% (7 of 23) of FMs had a low FRS who had initially had an intermediate or high FRS. Conclusions A patient-led referral strategy at the time of CICU admission led to a high rate of identification of previously undiagnosed CV risk factors in FMs. Implementing a similar referral program on a larger scale could identify a considerable burden of CV risk.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".