Depression, Type 2 Diabetes, and Poststroke Cognitive Impairment
Bibliographic record
Abstract
Background. Ten percent of stroke survivors develop dementia, which increases to more than a third after recurrent stroke. Other survivors develop less severe vascular cognitive impairment. In the general population, depression, and diabetes interact in predicting dementia risk, and they are both prevalent in stroke. Objective. To assess the cumulative association of comorbid depressive symptoms and type 2 diabetes with cognitive outcomes among stroke survivors. Methods. Multicenter observational cohort study of people within 6 months of stroke. Depression and cognitive status were screened using the Center for Epidemiological Studies Depression (CES-D) scale and the Montreal Cognitive Assessment (MoCA), respectively. Processing speed, executive function and memory were assessed using the Trail Making Test parts A and B, and the 5 Word Delayed Free Recall task. Results. Among 342 participants (age 67.0 ± 13.5 years, 43.3% female, 46 ± 35 days poststroke), the prevalence of type 2 diabetes was 32.2% and depressive symptoms (CES-D ≥16) were found in 40.6%. Diabetes and depressive symptoms increased the risk of severe cognitive impairment (MoCA <20) with adjusted odds ratio (OR) 2.12 (95% confidence interval [CI] 1.20-3.74, P = .010) for 1 comorbidity and OR 3.18 (95% CI 1.26-8.02, P = .014) for both comorbidities. Associated cognitive deficits included executive function ( F 1, 168 = 3.43, P = .035) but not processing speed ( F 1, 168 = 1.86, P = .16) or memory ( F 1, 168 = 0.82, P = .44). Conclusions. Diabetes and depressive symptoms were associated cumulatively with poorer cognitive screening outcomes poststroke, particularly deficits in executive function. Having 1 comorbidity doubled the odds of screening for severe cognitive impairment, having both tripled the odds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".