A Composite Measure of Gender and Its Association With Risk Factors in Patients With Premature Acute Coronary Syndrome
Bibliographic record
Abstract
OBJECTIVE: To create a gender index by using principal component analyses (PCA) and logistic regression, and to determine the association between gender, sex, and cardiovascular risk factors among patients with premature acute coronary syndrome (ACS). METHODS: GENESIS-PRAXY is a cohort study including ACS patients aged 55 years or below, and with ACS recruited between 2009 and 2013 from 26 centres across Canada, the United States, and Switzerland. A sample of 1075 patients was used for this study. Psychosocial variables assumed to differ between sexes (i.e., gender related) were included in PCA. Variables identified on retained components were included in logistic regressions where coefficient estimates of variables associated with sex were used to calculate a gender score. Cardiovascular risk factors were assessed using self-report and chart review data. RESULTS: After the inclusion of 26 psychosocial variables in PCA, we identified 17 variables within retained components; 7 of which were associated with sex in logistic regression. The gender distribution revealed that half of women had a more androgyne or masculine gender score, and 16% of men exhibited a more feminine gender score. In univariable analyses, feminine gender scores and female sex were associated with hypertension, diabetes, family history of cardiovascular disease (only gender), and depressive/anxious symptoms. In multivariable models including both gender score and sex, feminine gender score but not female sex was associated with the presence of risk factors. CONCLUSIONS: Sex and gender are distinct constructs, and the derived gender index offers a current and pragmatic option to measure gender within ACS populations. Our results further suggest that traditional sex differences in cardiovascular disease risk factors may be partly explained by patient's gender-related characteristics.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".