Concurrent use of statins and hormone therapy and risk of venous thromboembolism in postmenopausal women
Bibliographic record
Abstract
OBJECTIVE: Statins and hormone therapy (HT), often used concurrently in postmenopausal women, have antagonist effects on the risk of venous thromboembolism (VTE). This study aims to determine whether statins attenuate the increased VTE risk associated with HT. METHODS: We conducted a nested case-control study within a population-based cohort of women aged 50 to 79 years between January 1, 1987 and March 1, 2008, who were identified from the UK General Practice Research Database. Cases of VTE occurring during follow-up were identified and each matched with up to 10 controls from the cohort. Odds ratios (ORs) for the effects of concurrent HT and statin use on the risk of VTE were estimated using conditional logistic regression with interaction terms. RESULTS: The cohort included 955,582 postmenopausal women, with 23,505 cases of VTE matched with 231,562 controls. Regardless of any HT use, current use of statins was associated with a decreased risk of VTE (OR, 0.83; 95% CI, 0.78-0.87). The interaction between statin use and HT use was of borderline significance (P = 0.053). Consequently, among nonusers of statins, the risk of VTE was elevated with current use of oral estrogen and progestogen combinations (OR, 1.55; 95% CI, 1.45-1.66) but this risk was not elevated among users of statins (OR, 0.98; 95% CI, 0.56-1.73). There was no such modification of the OR with statins and other HT types and formulations. CONCLUSIONS: Statins could potentially attenuate the increased risk associated with HT combinations of oral estrogens and progestogens. This observation needs further confirmation in other large cohorts.
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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 | 0.004 |
| 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 | 0.001 | 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".