Rehabilitation for Patients After Stroke in a Tertiary Hospital: is it early and Intensive Enough?
Bibliographic record
Abstract
Background: This study aimed to evaluate the provision of rehabilitation to and the functional recovery of stroke survivors receiving acute care in a tertiary hospital. Methods: Analyses of medical records of stroke patients who were admitted to a main teaching hospital between the years 2006-2009. Variables studied were the demographics, clinical profiles, rehabilitation profiles and the patients' functional recovery at discharge, as measured on a modified Rankin Scale (mRS). Findings: Records of 557 patients; mean age 64 years, 53.7% females were analysed. Mean length of stay was 6.8 days SD=5.8 during which, 62.7% received daily rehabilitation. Mean time from stroke diagnosis to rehabilitation was 3.0 days SD=2.0; 51.0% of cases were seen after 48 hours of hospital admission. Rehabilitation was provided mainly by physiotherapy services to 71.6%, 64.7% and 52.5% of severe, moderate and mild cases, respectively. Mean mRS was 3.5, SD=0.9 and 53.0% had not regained any level of walking ability prior to discharge. Age and severity level significantly predicted mRS at discharge (F6,536=12.26, P<0.001). Conclusions: Provision of rehabilitation to acute stroke survivors in a tertiary hospital was sub-optimal. Establishment of dedicated stroke rehabilitation services is recommended to enable better functional gain in post-stroke survivors.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".