Quality of Life During and After Inpatient Stroke Rehabilitation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Very limited longitudinal data are available that assess the health-related quality of life (HRQOL) of stroke survivors after discharge from inpatient rehabilitation. The purpose of this research was to assess changes in HRQOL during inpatient rehabilitation and again 6 months after discharge. METHODS: This was a prospective study of all eligible patients admitted to an inpatient stroke rehabilitation hospital over a 3-year period. HRQOL was assessed by means of the Medical Outcomes Study 36-item Short Form (SF-36). Dependent t tests were used to compare the scores at admission and discharge and at discharge and 6 months. Changes in HRQOL were calculated for the period of admission to discharge and of discharge to the 6-month follow-up. RESULTS: Complete data were available for 85 patients. During rehabilitation, there were improvements in all 8 domains of the SF-36, with 5 attaining statistical significance. After discharge, 3 domains continued to improve, with 1 attaining statistical significance. However, there were marked and statistically significant declines in the other 5 domains of the SF-36. Feedback was obtained from a subset of the patients as to the reasons for these declines. CONCLUSIONS: Substantial gains in HRQOL during inpatient stroke rehabilitation may be followed by equally substantial declines in the 6 months after discharge. There is a need for longitudinal research into the HRQOL of stroke survivors and their families, as well as a need to ensure that adequate community services and support are available.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".