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 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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".