Abstract 12164: Gendered Social Determinants of Health and Risk of Major Adverse Outcomes in Atrial Fibrillation: An Analysis From the ESC-EHRA Eurobservational Research Programme in Atrial Fibrillation General Long-Term Registry
Bibliographic record
Abstract
Introduction: Atrial fibrillation (AF) is associated with a high risk of adverse outcomes. Social determinants of health (SDOH) are gendered (unevenly distributed between females and males) and associated with outcomes in cardiovascular diseases. Little is known about their impact in AF. We evaluated the association between gendered SDOH and adverse outcomes in AF patients. Methods: Data came from the ESC-EHRA EORP-AF General Long-Term Registry, a European AF prospective registry. Gendered SDOH included: education, living alone vs not, smoking, alcohol use, gender inequality index (GII), physical activity and quality of life measures. Study outcome was a composite of major adverse cardiovascular events and all-cause death. SDOH main effect was tested in multivariate logistic regressions and for a sex/GII interaction. Results: We studied 11,096 patients (mean (SD) age 69.2 (11.4) years; 40.7% females, median [IQR] CHA 2 DS 2 -VASc score 3 [2-4]). Most had secondary education, did not live alone, did not smoke or use alcohol, were physically inactive, had moderate quality of life, and lived in countries with gender equity. Multivariate analyses showed that gendered SDOH together with traditional risk factors were associated with the outcome (Figure 1). Higher education level and quality of life were associated with lower risk of adverse outcomes. Conversely, living alone and higher GII (larger gender inequity) were associated with worse outcomes. Females were found at lower risk, however this protective effect was reversed in countries with higher GII (sex-GII p- interaction 0.048). Conclusions: Gendered SDOH are associated with adverse outcomes in AF. Notably, gender inequity confers poorer outcomes in females with AF.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".