Trade-Offs between Effectiveness and Efficiency in Stroke Rehabilitation
Bibliographic record
Abstract
BACKGROUND: Most stroke research has studied rehabilitation effectiveness and rehabilitation efficiency separately and not investigated the potential trade-offs between these two indices of rehabilitation. AIMS: To determine whether there is a trade-off between independent factors of rehabilitation effectiveness and rehabilitation efficiency. METHODS: Using a retrospective cohort study design, we studied all stroke patients (n = 2810) from two sub-acute rehabilitation hospitals from 1996 to 2005, representing 87·5% of national bed-years during the same period. RESULTS: Independent predictors of poorer rehabilitation effectiveness and log rehabilitation efficiency were • older age • race-ethnicity • caregiver availability • ischemic stroke • longer time to admission • dementia • admission Barthel Index score, and • length of stay. Rehabilitation effectiveness was lower in females, and the gender differences were significantly lower in those aged ≤70 years (β -4·7 (95% confidence interval -7·4 to -2·0)). There were trade-offs between effectiveness and efficiency with respect to admission Barthel Index score and length of stay. An increase of 10 in admission Barthel Index score predicted an increase of 3·6% (95% confidence interval 3·2-4·0) in effectiveness but a decrease of 0·04 (95% confidence interval -0·05 to -0·02) in log efficiency (a reduction of efficiency by 1·0 per 30 days). An increase in log length of stay by 1 (length of stay of 2·7 days) predicted an increase of 8·0% (95% confidence interval 5·7-10·3) in effectiveness but a decrease of 0·82 (95% confidence interval -0·90 to -0·74) in log efficiency (equivalent to a reduction in efficiency by 2·3 per 30 days). For optimal rehabilitation effectiveness and rehabilitation efficiency, the admission Barthel Index score was 30-62 and length of stay was 37-41 days. CONCLUSIONS: There are trade-offs between effectiveness and efficiency during inpatient sub-acute stroke rehabilitation with respect to admission functional status and length of stay.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".