Abstract WP379: Sex-specific Effects of Comorbid Diabetes and Depression on Post-stroke Mortality in Individuals With Atrial Fibrillation
Bibliographic record
Abstract
Background: Comorbid diabetes and depression are highly prevalent in atrial fibrillation (AF) and increase the risk of stroke. Women with AF show higher mortality rates and have worse functional outcomes post-stroke. However, the sex-specific effects of comorbid diabetes and depression on mortality and other adverse outcomes in stroke patients with a history of AF is unclear. Methods: Prospectively collected consecutive patients with ischemic stroke and known AF presenting to designated stroke centres in Ontario (2003-2013). Multinomial regression was used to determine sex-specific associations between diabetes and depression and in-hospital mortality post-stroke in individuals with AF. Cox proportional hazards regression was used to estimate the adjusted hazard of long-term mortality post-stroke and competing risks models to estimate hazards of recurrent stroke/TIA, admission to long-term care, and incident dementia post-discharge. Results: Among 5082 stroke patients with known AF (median age=80, IQR:73-85), female patients were more likely to have comorbid depression than males (63.5% vs. 36.5%) and those with comorbid diabetes and depression were younger (77 yrs) and had more vascular history (HTN, CAD, hyperlipidemia) than those with AF only. For males, comorbid diabetes increased the likelihood of in-hospital mortality post-stroke by 53% (OR=1.53, 95% CI=1.16-2.02), after adjustment for stroke severity, demographic and clinical factors, while comorbid depression did not significantly impact in-hospital mortality and neither diabetes or depression affected in-hospital mortality post-stroke for females. However, diabetes was independently associated with increased hazard of long-term mortality for both female (HR=1.15, 95%CI=1.02-1.29) and male AF stroke patients (HR=1.35, 95%CI=1.19-1.53). No associations with recurrent stroke/TIA, institutionalization or dementia post-stroke were observed for either females or males. Conclusion: In stroke patients with known AF, comorbid diabetes but not depression was independently associated with increased in-hospital mortality for males and increased long-term mortality post-stroke for both females and males.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".