From Mars to Venus: Gender Differences in the Management and Outcomes of Acute Coronary Syndromes
Bibliographic record
Abstract
Ischemic heart disease remains a leading cause of morbidity and mortality in industrialized nations, and contributes substantially to healthcare expenditure worldwide. As the evidence base in acute coronary syndromes (ACS) has expanded dramatically over decades, longitudinal data demonstrate improvements in risk factor modification, organization of healthcare systems, and disease management that have substantially attenuated the adverse prognosis of both ST-segment elevation myocardial infarction (STEMI) and non-STsegment elevation ACS (NSTE-ACS). Nevertheless, discrepancies remain between genders, and women with ACS often sustain worse outcomes than men. In this review, we focus on the gender and sex-specific commonalities and differences in the pathophysiology, clinical presentations, diagnosis, and risk stratification of ACS. We highlight available data on the interactions between gender and efficacy of current pharmacological and interventional treatment for NSTE-ACS and STEMI. We also examine gender differences in the trends of clinical outcomes, and possible mechanisms that account for persistent care gaps where future efforts can be directed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".