Benchmarking length of stay for inpatient stroke rehabilitation without adversely affecting functional outcomes
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effects of introducing the practice of targeting a discharge date for patients admitted to an inpatient stroke rehabilitation unit on process and patient outcomes. DESIGN: Comparison of retrospective (control group n = 69) and prospective (experimental group n = 60) patients. METHODS: Rehabilitation professionals assessed both groups at admission and discharge using a standard-ized assessment toolkit. Benchmarks for length of rehabilitation stay (LoRS) were introduced based on median severity-specific LoRSs in the control group. The multidisciplinary team documented facilitators and obstacles affecting the prediction of patient benchmark attainment. Categorical variables were compared using a χ2 test with exact probabilities. Ordinal and continuous variables were analysed using rank-based non-parametric analysis of variance. Effect sizes were estimated using a relative treatment effect statistic. RESULTS: The mean combined length of stay in acute care and rehabilitation beds for the experimental group (82 days) was shorter (p = 0.0084) than that of the control group (103 days). This 21-day reduction in combined length of stay included a 10-day reduction in the mean time between stroke onset and admission to the stroke rehabilitation unit (p = 0.000014). Improvements in 6 func-tional and sensorimotor outcomes with rehabilitation were of similar magnitude in both groups, while Functional Independence Measure (FIMTM) efficiency improved (p = 0.022). The team was 87% successful in predicting which patients were discharged on the LoRS benchmark. CONCLUSION: Benchmarking the length of stay in rehabilitation resulted in reduced bed occupation and system costs without adversely affecting functional and sensorimotor patient outcomes.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".