Impairment in Health-Related Quality of Life among Community-Dwelling Stroke Survivors
Bibliographic record
Abstract
INTRODUCTION: Health utility instruments are increasingly being used to measure impairment in health-related quality of life (HRQoL) after stroke. Population-based studies of HRQoL after stroke and assessment of differences by age and functional domain are needed. METHODS: We used the Canadian Community Health Survey linked with administrative databases to determine HRQoL using the Health Utilities Index Mark 3 (HUI3) among those with prior hospitalization or emergency department visit for stroke and compared to controls without stroke. We used multivariable linear regression to determine the difference in HUI3 between those with stroke and controls for the global index and individual attributes, with assessment for modification by age (<60, 60-74, and 75+ years) and sex, and we combined estimates across survey years using random effects meta-analysis. RESULTS: Our cohort contained 1240 stroke survivors and 123,765 controls and was weighted to be representative of the Canadian household population. Mean health utility was 0.63 (95% confidence interval [CI] 0.58, 0.68) for those with stroke and 0.83 (95% CI 0.82, 0.84) for controls. There was significant modification by age, but not sex, with the greatest adjusted reduction in HUI3 among stroke respondents aged 60-74 years. Individual HUI3 attributes with the largest reductions in utility among stroke survivors compared to controls were mobility, cognition, emotion, and pain. CONCLUSIONS: In this population-based study, the reduction in HUI3 among stroke survivors compared to controls was greatest among respondents aged 60-74, and in attributes of mobility, cognition, emotion, and pain. These results highlight the persistent impairment of HRQoL in the chronic phase of stroke and potential targets for community support.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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, 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".