The impact of gender on the treatment and outcomes of patients with early reinfarction after fibrinolysis: insights from ASSENT-2⋆
Bibliographic record
Abstract
AIMS: To assess gender differences in patients with early reinfarction after fibrinolysis for acute myocardial infarction (AMI) and the impact of these differences on treatment and outcomes. METHODS AND RESULTS: We studied 3.7% of men (n=481) and 4.8% of women (n=189) with early reinfarction after fibrinolysis for AMI in the ASSENT-2 trial of 16,949 patients. Women with reinfarction were older and more often had hypertension, diabetes, and major bleeding prior to reinfarction. Despite adjustment for these differences, women with reinfarction were less likely to receive repeat fibrinolytic therapy (OR: 0.55; 95% CI: 0.37-0.84). Aggressive treatment by either repeat fibrinolysis or urgent revascularization was associated with reduced 1-year mortality irrespective of gender. Death within 24 h of reinfarction was more frequent in women and accounted for a greater proportion of their 1-year mortality (56.0 vs 34.8%; p=0.02). The excess mortality in women at 1 year (27.3 vs 19.9%; p=0.045) was eliminated after adjustment for gender differences in baseline risk profile. CONCLUSION: Women with early reinfarction following fibrinolysis for AMI had more frequent early death and were managed less aggressively. These findings suggest the need for increased awareness and timely intervention in these patients.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".