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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".