Association of hormone therapy with cardiovascular events in females using statins for prevention
Bibliographic record
Abstract
OBJECTIVE: The association of hormone therapy (HT) combined with statin use for primary prevention of cardiovascular disease remains uncertain. This study aimed to assess the effect of HT, initiated before the age of 60 years, on all-cause mortality and cardiovascular events in females using statins for primary prevention. METHOD: This population-based, retrospective cohort study included all females aged 40-60 years in British Columbia, Canada, who used statins for primary prevention. The exposure was defined as systemic HT, including estrogen alone or combined with a progestogen, excluding local preparations of estrogen. The study used Cox proportional hazards models from the study start date to the outcome. RESULTS: After exact matching on age using up to a 1-to-4 match, 685 (20%) of the 3,425 statin users initiated HT within the first year of follow-up. HT use was not significantly associated with all-cause mortality after adjusting for confounders (adjusted hazard ratio [aHR], 1.17; 95% confidence interval [CI], 0.87-1.58). Similarly, for the secondary outcome of composite cardiovascular events, HT use did not significantly increase risk (aHR, 0.95; 95% CI, 0.75-1.20). CONCLUSION: This study found that HT, when initiated before age 60 years, was not associated with an increased risk of all-cause mortality or cardiovascular events in females using statins for primary prevention.
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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 | 0.000 | 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".