Gender differences in cardiovascular risk, treatment, and outcomes: a post hoc analysis from the REWIND trial
Bibliographic record
Abstract
Objectives. To assess whether the use of cardioprotective therapies for type 2 diabetes varies by gender and whether the risk of cardiovascular events is higher in women versus men in the REWIND trial, including an international type 2 diabetes patient population with a wide range of baseline risk. Design. Gender differences in baseline characteristics, cardioprotective therapy, and the achieved clinical targets at baseline and two years were analyzed. Hazards for cardiovascular outcomes (fatal/nonfatal stroke, fatal/nonfatal myocardial infarction, cardiovascular death, all-cause mortality, and heart failure hospitalization), in women versus men were analyzed using two Cox proportional hazard models, adjusted for randomized treatment and key baseline characteristics respectively. Time-to-event analyses were performed in subgroups with or without history of cardiovascular disease using Cox proportional hazards models that included gender, subgroup, randomized treatment, and gender-by-subgroup interactions. Results. Of 9901 participants, 46.3% were women. Significantly fewer women than men had a cardiovascular disease history. Although most women met treatment targets for blood pressure (96.7%) and lipids (72.8%), fewer women than men met the target for cardioprotective therapies at baseline and after two years, particularly those with prior cardiovascular disease, who used less renin-angiotensin-aldosterone system inhibitors, statins, and aspirin than men. Despite these differences, women had lower hazards than men for all outcomes except stroke. No significant gender and cardiovascular disease history interactions were identified for cardiovascular outcomes. Conclusions. In REWIND, most women met clinically relevant treatment targets, but in lower proportions than men. Women had a lower risk for all cardiovascular outcomes except stroke. Clinical trials.gov registration number: NCT01394952
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".