Inpatient rehabilitation following stroke: amount of therapy received and associations with functional recovery
Bibliographic record
Abstract
PURPOSE: Canada's Best Practice Recommendations for Stroke Care state that a minimum of one hour per day of each of the relevant core therapies be provided to patients admitted for inpatient rehabilitation. We examined whether this standard was met on a single, specialized stroke rehabilitation unit and if amount of therapy was an independent contributor to functional improvement. METHODS: One-hundred and twenty-three, consecutive patients admitted to a 30-bed stroke rehabilitation program over a 6-month period with the confirmed diagnosis of stroke, were included. Workload measurement data were used to estimate the amount of therapy that patients received from core therapists during their inpatient stay. A multivariable model to predict Functional Independence Measure (FIM) gains achieved was also developed using variables that were significantly correlated with functional gain on univariate analysis. RESULTS: On average, patients received 37 min of active therapy from both physiotherapists (PT) and occupational therapists (OT) and 13 min from speech-language pathologists per day. Admission FIM, length of stay, total OT and PT therapy time (hrs) were significantly correlated with FIM gain. In the final model, which explained 35% of the variance, admission FIM score and total amount of occupational therapy (OT) emerged as significant predictors of FIM gain. CONCLUSIONS: Patients admitted to a specialized rehabilitation unit received an average of 37 min a day engaged in therapeutic activities with both occupational and physical therapists. Although this value did not reach the standard of one hour, total amount of OT time contributed significantly to gains in FIM points during hospital stay.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".