Cognition and Independence Drive Health-Related Quality of Life in Stroke Survivors: A Predictive Model from District Narowal
Bibliographic record
Abstract
Background: Stroke imposes profound physical, cognitive, and psychosocial burdens, leading to sustained reductions in health-related quality of life (QOL). Understanding how cognition and functional independence jointly influence QOL is critical for improving rehabilitation outcomes, especially in low-resource settings where rapid clinical assessment tools are essential. Objective: To quantify the independent and combined effects of cognitive function and functional independence on domain-specific QOL among stroke survivors and to evaluate whether age contributes incremental predictive value. Methods: A cross-sectional predictive study was conducted among 100 stroke survivors at District Headquarter Hospital, Narowal. Health-related QOL was assessed using the WHOQOL-BREF, cognition using the Montreal Cognitive Assessment (MOCA), and functional independence using the Barthel Index (BI). Hierarchical linear regressions examined associations between MOCA, BI, age, and the four QOL domains. Model validity was tested through 10-fold cross-validation. Results: BI and MOCA showed strong positive correlations with all QOL domains (r = 0.41–0.74, p < 0.001). BI independently explained 46–55% of variance in QOL domains, while the addition of MOCA improved explained variance up to 61% (ΔR² = 0.06, p = 0.002). Age had a minor, nonsignificant effect. Cross-validation confirmed model stability (R² = 0.40–0.59). Conclusion: Functional independence and cognition are key determinants of post-stroke QOL, highlighting the value of integrating BI and MOCA into rehabilitation planning to enhance multidimensional recovery in resource-limited healthcare settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".