Abstract 17126: Tackling Cardiovascular Disease Risk in Primary Care: Does Sex Matter?
Bibliographic record
Abstract
Purpose: To ascertain whether men and women respond differently to an intervention intended to decrease cardiovascular disease (CVD) risk in primary care (PC) through individualized systematic risk factor screening, risk-weighted behavioral counseling and pharmacological treatment. Methods: We studied two geographically diverse PC practices in Nova Scotia, Canada, with differing reimbursement models vs a comparison group. Patients completed a health risk assessment (HRA) and readiness to change assessment that triggered an intervention individualized around both parameters with 1-year follow-up and a final HRA. The primary endpoint was the proportion of subjects with moderate and high baseline 10-year Framingham Risk Scores reducing risk by 10% and 25%, respectively. Sex-differences in response to the intervention were explored. Results: 1509 intervention patients enrolled; 72% completed the study. Results are presented for 561 subjects with moderate or high baseline Framingham risk but no diabetes or established CVD. Mean age was 55.4 years; 57.8% were female. 43% vs 31% controls achieved the primary endpoint (p=0.06, ARR = 12%, RRR = 38%, NNT=9). Females (ARR=15%, RRR=46%, NNT= 7) did better than males (ARR=5.2%, RRR=17.2%, NNT=20). When comparing sexes, there was significantly greater reduction in systolic blood pressure (p< 0.001) and a higher increase in HDL cholesterol (p=0.04) for females. Significantly more (p < 0.0001) females changed their metabolic syndrome status (prevalence 93.5% vs 58.6% pre and post intervention, respectively) vs. males (59.5% vs. 46.8% pre and post intervention, respectively). The specific metabolic syndrome risk factors that drove the difference included waist circumference (p <0.0001), HDL cholesterol (p=0.03) and blood pressure (p=0.04). These results were obtained largely through lifestyle modification; drug use did not change significantly. Conclusion: In an intervention to improve CVD risk in primary care, women did better than men. Differences were seen especially around improved metabolic risk, predominantly as a result of lifestyle change. These findings suggest that prevention programs may need to modify their approaches according to patient sex.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".