Developing a gender measure and examining its association with cardiovascular diseases incidence: a 28-year prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases (CVD) are the leading cause of morbidity and mortality worldwide. Examining gender (socio-cultural) in addition to sex (biological) is required to untangle socio-cultural characteristics contributing to inequities within or between sexes. This study aimed to develop a gender measure including four gender dimensions and examine the association between this gender measure and CVD incidence, across sexes. METHODS: A cohort of 9188 white-collar workers (49.9% females) in the Quebec region was recruited in 1991-1993 and follow-up was carried out 28 years later for CVD incidence. Data collection involved a self-administered questionnaire and extraction of medical-administrative CVD incident cases. Cox proportional models allowed calculations of hazard ratios (HR) and 95% confidence intervals (CI), stratified by sex. RESULTS: Sex and gender were partly independent, as discordances were observed in the distribution of the gender score across sexes. Among males, being in the third tertile of the gender score (indicating a higher level of characteristics traditionally ascribed to women) was associated with a 50% CVD risk increase compared to those in the first tertile (HR = 1.50; 95% CI: 1.24 to 1.82). This association persisted after adjustment for several CVD risk factors (HR = 1.42; 95% CI: 1.16 to 1.73). Conversely, no statistically significant association between the third tertile of the gender score and CVD incidence was observed in females (HR = 0.79, 95% CI: 0.60-1.05). CONCLUSIONS: The findings suggested that males within the third tertile of the gender score were more likely to develop CVD, while females with those characteristics did not exhibit an increased risk. These findings underline the necessity for clinical and population health research to integrate both sex and gender measures, to further evaluate disparities in cardiovascular health and enhance the inclusivity of prevention strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".