Impact of Sex‐ and Gender‐Related Factors on Length of Stay Following Non–ST‐Segment–Elevation Myocardial Infarction: A Multicountry Analysis
Bibliographic record
Abstract
Background Gender‐related factors are psycho‐socio‐cultural characteristics and are associated with adverse clinical outcomes in acute myocardial infarction, independent of sex. Whether sex‐ and gender‐related factors contribute to the substantial heterogeneity in hospital length of stay (LOS) among patients with non–ST‐segment–elevation myocardial infarction remains unknown. Methods and Results This observational cohort study combined and analyzed data from the GENESIS‐PRAXY (Gender and Sex Determinants of Cardiovascular Disease: From Bench to Beyond Premature Acute Coronary Syndrome study), EVA (Endocrine Vascular Disease Approach study), and VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI [Acute Myocardial Infarction] Patients study) cohorts of adults hospitalized across Canada, the United States, Switzerland, Italy, Spain, and Australia for non–ST‐segment–elevation myocardial infarction. In total, 5219 participants were assessed for eligibility. Sixty‐three patients were excluded for missing LOS, and 2938 were excluded because of no non–ST‐segment–elevation myocardial infarction diagnosis. In total, 2218 participants were analyzed (66% women; mean±SD age, 48.5±7.9 years; 67.8% in the United States). Individuals with longer LOS (51%) were more likely to be White race, were more likely to have diabetes, hypertension, and a lower income, and were less likely to be employed and have completed secondary education. No univariate association between sex and LOS was observed. In the adjusted multivariable model, age (0.62 d/10 y; P <0.001), unemployment (0.63 days; P =0.01), and some of countries included relative to Canada (Italy, 4.1 days; Spain, 1.7 days; and the United States, −1.0 days; all P <0.001) were independently associated with longer LOS. Medical history mediated the effect of employment on LOS. No interaction between sex and employment was observed. Longer LOS was associated with increased 12‐month all‐cause mortality. Conclusions Older age, unemployment, and country of hospitalization were independent predictors of LOS, regardless of sex. Individuals employed with non–ST‐segment–elevation myocardial infarction were more likely to experience shorter LOS. Sociocultural factors represent a potential target for improvement in health care expenditure and resource allocation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".