Cognitive and Emotional Impairments in Acute Post-Stroke Patients—A Cross-Sectional Study
Bibliographic record
Abstract
Background and Objectives: Stroke is widely recognised for its physical consequences. However, cognitive and emotional impairments, such as depression, anxiety, and vascular cognitive impairment (VCI), are often under-recognised and under-treated. Our study aimed to identify and characterise cognitive and emotional sequelae in patients hospitalised for acute ischemic stroke. Materials and Methods: We conducted a cross-sectional study involving 73 patients within seven days of an acute ischemic stroke. Patients were assessed using the National Institutes of Health Stroke Scale (NIHSS), modified Rankin Scale (mRS), Montreal Cognitive Assessment (MoCA), Hachinski Ischemic Score (HIS), and the Clinical Assessment of Depression (CAD) questionnaire, which includes four subscales (Depressed Mood (DM), Anxiety/Worry, Disinterest, and Physical Fatigue). K-means clustering was applied to ten standardised clinical and psychometric variables. In addition, multiple linear regression was performed to determine independent predictors of cognitive and affective outcomes, with MoCA and CAD-DM as dependent variables. Results: Three distinct patient profiles emerged: (1) Mild Impairment Profile, characterised by minimal cognitive or emotional symptoms; (2) Depressive Profile, marked by elevated emotional symptom scores despite mild physical impairment; and (3) Vascular Cognitive Impairment Profile, comprising older patients with the most severe cognitive and functional deficits. ANOVA confirmed significant differences between groups in NIHSS, mRS, MoCA, HIS, and CAD scores, but not for age or education. Linear regression revealed that older age (β = –0.10, p = 0.012) and higher NIHSS at discharge (β = –0.72, p = 0.020) predicted lower MoCA scores, whereas years of education (β = 0.58, p = 0.013) predicted better cognition (R2 = 0.29). No demographic or clinical factors predicted depressive symptoms (all p > 0.29). Conclusions: Our study highlights the heterogeneity of post-stroke outcomes. Neuropsychiatric impairments may be present even in patients with minimal physical deficits and require targeted evaluation and management.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".