Factors Affecting Quality of Life among Post-Stroke Patients in the Sub-Himalayan Region
Bibliographic record
Abstract
Abstract Background Stroke is one of the most debilitating conditions contributing to significant disability and death globally. Identifying risk factors for quality of life (QoL) will enable to improve home-based rehabilitation in post-stroke phase. Objective This study was aimed to identify the risk factors of QoL in stroke patients in the sub-Himalayan region. Materials and Methods A cross-sectional hospital-based study assessed the QoL among stroke patients within a week after the onset of acute stroke and then re-evaluated at 3 months. World Health Organization QoL-BREF, Beck Depression Inventory, the Barthel Index, and Montreal Cognitive Assessment (MOCA) were used to seek data on QoL, depression, cognitive, and functional dependence status, respectively. Appropriate statistics were used to compute the results. Results In total, 129 stroke patients recruited, out of which 102 returned to a 3-month follow-up. QoL, MOCA, disability index, and depression score were compared using Wilcoxon Singed-rank test. In multivariate analysis, depression and disability together predicted 60% of the variance for physical QoL ( p < 0.0001). Similarly, poststroke depression and disability together predicted 61% of the variance for psychological QoL ( p < 0.0001) in stroke patients. Conclusion Findings indicated that depression and disability are leading risk factors of QoL in stroke patients. Early identification of poststroke depression and functional dependence status is, therefore, essential to devise screening procedure and to develop targeted intervention to improve rehabilitation outcomes.
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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 | 0.001 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".