Abstract TP209: Women Report More Symptoms of Depression Post-Stroke With Routine Screening
Bibliographic record
Abstract
Introduction: Stroke is a leading cause of mortality and morbidity. Important sex differences have been observed in stroke outcomes. Compared to men, women have poorer outcomes, but the reasons for this observation are unclear. Depression affects approximately one-third of patients post-stroke and is associated with poor outcomes. We aimed to determine any sex differences in the prevalence of symptoms of depression in stroke survivors. Methods: We identified a cohort of adult patients with stroke or TIA from the prospective, multi-centered Depression, Obstructive sleep apnea, and Cognitive impairment (DOC) study. All patients systematically completed a depression screening tool (Patient Health Questionnaire-2). A score of 4 or more out of 6 indicates that the patient is at high risk of depression. We used logistic regression with and without adjustment for age, event type (TIA or ischemic stroke), education level, vascular risk factors, cognitive symptoms, cancer, and disability to determine the association between sex and risk of depression. Results: We included 2,379 patients (mean age 68 years (SD = 14.8), 1310 men (55.1%). There were 222 (9.3%) patients at high risk for depression. Women were more likely to be at high risk (Odds Ratio OR 1.4, 95% CI [1.1, 1.9]) in univariable analysis. This association remained true after adjustment for covariates (OR 1.6, 95% CI [1.2, 2.1]). Conclusion: With systematic screening post-stroke, women are at higher risk for depression than men, even after controlling for baseline differences. Further research into whether our findings mediate the sex differences in outcomes post-stroke and the implications on treatment of depression post-stroke is needed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.012 | 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".