Determinants of Post-Stroke Cognitive Impairment in a Hospital-Based Sialkot Cohort
Bibliographic record
Abstract
Background: Post-stroke cognitive impairment (PSCI) is one of the most disabling sequelae of stroke, contributing to functional dependence, poor rehabilitation outcomes, and reduced quality of life. Despite a rising burden of stroke in Pakistan, there remains limited evidence on the prevalence and determinants of PSCI in secondary-care hospital settings. Objective: To assess the frequency and predictors of post-stroke cognitive impairment among survivors in Sialkot hospitals and to identify the independent socio-demographic and clinical factors influencing cognitive outcomes. Methods: An analytical cross-sectional study was conducted among 100 stroke survivors and 100 healthy controls recruited from four major hospitals in Sialkot between January and June 2024. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA), stroke severity by the National Institutes of Health Stroke Scale (NIHSS), and functional status by the Barthel Index. Data were analyzed using SPSS v25, applying t-tests, ANOVA, correlation, and multiple linear regression to determine independent predictors of MoCA scores. Results: Cognitive impairment (MoCA <26) was observed in 78% of stroke survivors versus 12% of controls (p<0.001). Lower MoCA scores correlated significantly with older age (r=-0.47), higher NIHSS (r=-0.59), and lower Barthel Index (r=+0.63). Education, stroke severity, and functional independence independently predicted cognitive performance (adjusted R²=0.61). Conclusion: Post-stroke cognitive impairment is highly prevalent among stroke survivors in Sialkot and is primarily influenced by age, education, and neurological severity. Routine cognitive screening and integrated rehabilitation strategies are recommended to enhance recovery and independence.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".