Rehabilitation Outcomes of Patients With Severe Disability Poststroke
Bibliographic record
Abstract
Objective: To characterize rehabilitation outcomes of patients with severe poststroke motor impairment (MI) and develop a predictive model for treatment failure. Design: Retrospective cohort study. Correlates of treatment failure, defined as the persistence of severe MI after rehabilitation, were identified using logistic regression analysis. Then, an integer-based scoring rule was developed from the logistic model. Setting: Three specialized inpatient rehabilitation facilities. Participants: Patients (N=1265) classified as case-mix groups (CMGs) 0108, 0109, and 0110 of the Medicare classification system. Interventions: Not applicable. Main outcome measure: Change in the severity of MI, as assessed by the FIM, from admission to discharge. Results: Median FIM-motor (FIM-M) score increased from 17 (interquartile range [IQR] 14-23) to 38 (IQR, 25-55) points. Median proportional recovery, as expressed by FIM-M effectiveness, was 26% (IQR, 12-47). Median FIM-M change was 18 (IQR, 9-34) points. About 38.5% patients achieved the minimal clinically important difference. Eighteen point six percent and 32.0% of the patients recovered to a stage of either mild (FIM-M ≥62) or moderate (FIM-M 38-61) MI, respectively. All between-CMG differences were statistically significant. Outcomes have also been analyzed according to classification systems used in Australia and Canada. The scoring rule had an area under the curve of 0.833 (95% confidence interval, 0.808-0.858). Decision curve analysis displayed large net benefit of using the risk score compared with the treat all strategy. Conclusions: This study provides a snapshot of rehabilitation outcomes in a large cohort of patients with severe poststroke MI, thus filling a gap in knowledge. The scoring rule accurately identified the patients at risk for treatment failure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".