Quality of Life Profiles in Brain Stroke Survivors Versus Community Controls: A Case–Control Study from an Under-Resourced District Hospital
Bibliographic record
Abstract
Background: Brain stroke is a leading cause of disability and mortality globally, profoundly impacting quality of life (QOL), particularly in under-resourced settings like rural Pakistan where rehabilitation access is limited. Few studies provide case-control comparisons of QOL, cognitive function, and functional independence in district-level hospitals, leaving gaps in understanding localized deficits and screening strategies. Objective: To compare QOL, cognitive function, and functional independence between stroke survivors and community controls at a district hospital in Narowal, Pakistan, and identify simple screening thresholds for low QOL. Methods: In a case-control study from September to November 2023 at District Head Quarter Hospital Narowal, 100 stroke survivors and 100 community controls were assessed using the WHOQOL-BREF, Montreal Cognitive Assessment (MoCA), and Barthel Index (BI). Independent t-tests, two-way ANOVA for age-stratified effects, and ROC analyses were conducted to compare groups and derive cut-offs for low QOL (≤25th percentile of control scores). Results: Stroke survivors showed significantly lower scores across all WHOQOL-BREF domains (p<0.01, Cohen’s d=1.57–3.02), MoCA (mean=15.35 vs. 29.15, p<0.001), and BI (mean=44.90 vs. 98.55, p<0.001), with older survivors (age >65) exhibiting steeper QOL declines (p<0.05). MoCA cut-offs (15–18) and BI cut-offs (45–55) achieved AUCs of 0.75–0.87 for detecting low QOL. Conclusion: Stroke survivors in rural Pakistan face substantial QOL, cognitive, and functional deficits, accentuated by age, necessitating accessible screening and targeted rehabilitation to improve outcomes. Keywords: Stroke, Quality of Life, Cognitive Impairment, Functional Independence, Case-Control Study, Pakistan
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".