The influence of gender on the effects of aspirin in preventing myocardial infarction
Bibliographic record
Abstract
BACKGROUND: There is considerable variation in the effect of aspirin therapy reducing the risk of myocardial infarction (MI). Gender could be a potential explanatory factor for the variability. We conducted a systematic review and meta-analysis to determine whether gender mix might play a role in explaining the large variation of aspirin efficacy across primary and secondary MI prevention trials. METHODS: Randomized placebo-controlled clinical trials that examined the efficacy of aspirin therapy on MI were identified by using the PUBMED database (1966 to October 2006). Weighted linear regression technique was used to determine the relationship between log-transformed relative risk (RR) of MI and the percentage of male participants in each trial. The reciprocal of the standard error of the RR in each trial (1/SE) was used as the weight. RESULTS: A total of 23 trials (n = 113 494 participants) were identified. Overall, compared with placebo, aspirin reduced the risk of non-fatal MI (RR = 0.72, 95% confidence interval (CI) 0.64-0.81, p < 0.001) but not of fatal MI (RR = 0.88, 95% CI 0.75-1.03, p = 0.120). A total of 27% of the variation in the non-fatal MI results could be accounted for by considering the gender mix of the trials (p = 0.017). Trials that recruited predominantly men demonstrated the largest risk reduction in non-fatal MI (RR = 0.62, 95% CI 0.54-0.71), while trials that contained predominately women failed to demonstrate a significant risk reduction in non-fatal MI (RR = 0.87, 95% CI 0.71-1.06). CONCLUSION: Gender accounts for a substantial proportion of the variability in the efficacy of aspirin in reducing MI rates across these trials, and supports the notion that women might be less responsive to aspirin than men.
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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 | 0.027 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".