The Contribution of Gender to Apparent Sex Differences in Health Status Among Patients with Coronary Artery Disease
Bibliographic record
Abstract
BACKGROUND: While it has been identified that gender (socially manufactured roles, behaviors, expressions, and identities) plays a central role in men's and women's health, the distinction between gender and sex (biological attributes) has largely been ignored in health science research. The purpose of this study was to look at the unique contributions of sex, age, and the Gender Index (GI) to baseline health status in a cohort of patients with coronary artery disease (CAD). PARTICIPANTS AND METHODS: Questions that comprised the GI were included in the follow-up questionnaire sent to patients in the APPROACH registry. To examine the relationship between sex, gender, and health status, a sequential linear regression modeling approach was used. RESULTS: A total of 632 patients completed the GI between July and August 2015. The women were significantly older (68 years vs. 66 years, p = 0.02) and significantly more likely to have hypertension (50.8% vs. 38.8%, p = 0.02) compared to the men. Women reported significantly lower mean Seattle Angina Questionnaire (SAQ) scores compared to men. The inclusion of age into the models did not change the relationship between sex and the SAQ scales. However, the inclusion of the GI attenuated the relationship between sex and the SAQ scale scores. CONCLUSIONS: Our results support the concept that sex differences in health status outcomes may be better explained by patient's gender-related characteristics, than biological sex characteristics. More importantly, the GI offers a pragmatic composite score to assess the effects of psychosocial factors that researchers interested in measuring gender could use in studies of subjects with CAD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".