Sex and gender in cardiovascular medicine: presentation and outcomes of acute coronary syndrome
Bibliographic record
Abstract
Although health disparities in women presenting with acute coronary syndrome (ACS) have received growing attention in recent years, clinical outcomes from ACS are still worse for women than for men. Women continue to experience higher patient and system delays and receive less aggressive invasive treatment and pharmacotherapies. Gender- and sex-specific variables that contribute to ACS vulnerability remain largely unknown. Notwithstanding the sex differences in baseline coronary anatomy and function, women and men are treated the same based on guidelines that were established from experimental and clinical trial data over-representing the male population. Importantly, younger women have a particularly unfavourable prognosis and a plethora of unanswered questions remains in this younger population. The present review summarizes contemporary evidence for gender and sex differences in vascular biology, clinical presentation, and outcomes of ACS. We further discuss potential mechanisms and non-traditional risk conditions modulating the course of disease in women and men, such as unrecognized psychosocial factors, sex-specific vascular and neural stress responses, and the potential impact of epigenetic modifications.
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 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) | 0.001 | 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 | 0.000 | 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".