Testosterone Therapy and Risk of Myocardial Infarction: A Pharmacoepidemiologic Study
Bibliographic record
Abstract
BACKGROUND: Recent studies have provided conflicting and controversial results about the risk of cardiovascular events, including myocardial infarction (MI), with testosterone replacement therapy (TRT). The potential adverse effects of different TRT formulations and duration of therapy on MI risk are unknown. METHODS: We performed a case-control study within a cohort of 934,283 men aged 45-80 from the IMS LifeLink Health Plan Claims Database. For each case of MI, four controls were identified using density-based sampling. Rate ratios (RRs) were computed for current and past TRT users. As a sensitivity analysis, the risk of MI before and after the start of a first-time TRT prescription in the same patient was also computed. RESULTS: We identified 30,066 MI cases and 120,264 corresponding controls. Current use of TRT was not associated with an increased risk of MI (RR 1.01, 95% confidence interval [CI] 0.89-1.16); first-time users did show an increased risk (RR 1.41, 95% CI 1.06-1.87; number needed to harm 305). There was no association between MI and past TRT users and no differences among the different formulations. The RRs for current use and first-time use of TRT in men with a previous history of coronary artery disease were 1.05 (95% CI 0.79-1.41) and 1.78 (95% CI 0.93-3.40), respectively. CONCLUSION: In this large observational study, an association between MI and past or current TRT use was not found. However, a statistically significant association was observed between first-time TRT exposure and MI, although the absolute risk was low.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".