Postmenopausal Hormone Therapy and Risk of Stroke
Bibliographic record
Abstract
BACKGROUND: Observational studies have shown that postmenopausal hormone therapy may increase, decrease, or have no effect on the risk of stroke. To date, no clinical trial has examined this question. To investigate the relation between estrogen plus progestin therapy and risk of stroke among postmenopausal women, we analyzed data collected from the Heart & Estrogen-progestin Replacement Study (HERS), a secondary coronary heart disease prevention trial. METHODS AND RESULTS: Postmenopausal women (n=2763) were randomly assigned to take conjugated estrogen plus progestin or placebo. Primary outcomes for these analyses were stroke incidence and stroke death during a mean follow-up of 4.1 years. The number of women with strokes was compared with the number of women without strokes. A total of 149 women (5%) had 1 or more strokes, 85% of which were ischemic, resulting in 26 deaths. Hormone therapy was not significantly associated with risk of nonfatal stroke (relative hazard [RH] 1.18; 95% CI 0.83 to 1.66), fatal stroke (RH 1.61; 95% CI 0.73 to 3.55), or transient ischemic attack (RH 0.90; 95% CI 0.57 to 1.42). Independent predictors of stroke events included increasing age, hypertension, diabetes, current cigarette smoking, and atrial fibrillation. Black women were at increased risk compared with white women, and unexpectedly, body mass index was inversely associated with stroke risk. CONCLUSIONS: Hormone therapy with conjugated equine estrogen and progestin had no significant effect on the risk for stroke among postmenopausal women with coronary disease.
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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 | 0.000 | 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) | 0.002 | 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".