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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".