Sex-based disparities in cardioprotective medication use in adults with diabetes
Bibliographic record
Abstract
OBJECTIVE: The identification of sex-based disparities in the use of effective medications in high-risk populations can lead to interventions to minimize disparities in health outcomes. The objective of this study was to determine sex-specific rates of cardioprotective medication use in a large population-level administrative-health database from a universal-payer environment. RESEARCH DESIGN AND METHODS: This observational, population-based cohort study used provincial administrative data to compare the utilization of cardioprotective medications between women and men in the first year following a diabetes diagnosis. Competing risks regression was used to calculate crude and adjusted sub-hazard ratios for time-to-first angiotensin-converting-enzyme inhibitor, angiotensin receptor blocker, or statin dispensations. RESULTS: There were 15,120 (45.4%) women and 18,174 (54.6%) men with diabetes in the study cohort. Overall cardioprotective medication use was low for both primary and secondary prevention for both women and men. In the year following a diabetes diagnosis, women were less likely to use a statin relative to men (adjusted sub-hazard ratio [aSHR] 0.90, 95% confidence interval [CI] 0.85 to 0.96), angiotensin-converting-enzyme inhibitors (aSHR 0.90, 95% CI 0.86 to 0.94), or any cardioprotective medication (aSHR 0.93, 95% CI 0.90 to 0.97). CONCLUSIONS: Cardioprotective medication use was not optimal in women or men. We also identified a health care gap with cardioprotective medication use being lower in women with diabetes compared to men. Closing this gap has the potential to reduce the impact of cardiovascular disease in women with diabetes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".