Gender-Related Factors Associated With Outcomes of Acute Coronary Syndrome in Young Female Patients
Bibliographic record
Abstract
Acute coronary syndrome (ACS) remains a significant global health concern, with a growing recognition of its impact on young adults, particularly young female adults. Although gender-related factors, defined as a social construct that encompasses 4 distinct dimensions (gender roles, gender identity, gender relations, and institutionalized gender) are undoubtedly relevant across age groups, young female patients with ACS face specific challenges and disparities in outcomes, compared to other populations. This narrative review examines the role of gender-related factors-specifically, gender roles, gender identity, gender relations, and institutionalized gender-in influencing objective and subjective ACS outcomes in young female patients. In the 5 articles identified, the objective outcomes included hospital readmission, "door-to-electrocardiography" time, and coronary atherosclerosis progression. Subjective outcomes, such as physical and mental functional status, quality of life, physical limitations, and vital exhaustion, were also examined. Being employed, which is a gender role, emerged as a protective factor against hospital readmission. Gender identity factors such as depression and stress were correlated with negative outcomes, and anxiety influenced "door-to-electrocardiography" times. Institutional factors, including income disparities, affected readmission likelihood. Strong social support decreased physical limitations post-ACS, whereas financial challenges and lower education negatively impacted quality of life and vital exhaustion. These findings underscore the intricate interplay of gender dimensions in shaping ACS outcomes among young female patients. Integrating these insights into clinical practice and research can enhance care, mitigate disparities, and foster improved cardiovascular health in this vulnerable population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".