Efficacy of a functionally-based neurorehabilitation programme: A retrospective case-matched study of rehabilitation outcomes following traumatic brain injury
Bibliographic record
Abstract
OBJECTIVE: To investigate the efficacy of a functionally-based neurorehabilitation programme. DESIGN: Retrospective, case-matched cohort design. SETTING: An inpatient acquired brain injury (ABI) service at a post-acute rehabilitation facility. In 2001, a functionally-based rehabilitation model was introduced that streams patients into neurocognitive (NC) and neurophysical (NP) treatment groups based on predominant functional deficits. METHODS: Sixty-nine patients with TBI admitted to the ABI service before implementation of the functionally-based programme comprised a historical control group. These patients were individually case-matched to 69 participants admitted after the functionally-based programme began. Rehabilitation outcomes were compared between matched patients treated before and after implementation of the programme. OUTCOME MEASURES: Rehabilitation length of stay (RLOS), Functional Independence Measure (FIM) and Disability Rating Scale (DRS) at rehabilitation discharge and FIM efficiency. RESULTS: At discharge, the NP-stream had a significantly higher FIM motor efficiency compared to the historical control (0.41 vs. 0.29; p = 0.01). The NC-stream had significantly less disability, as measured by the DRS, compared to its control (3.63 vs. 5.05; p = 0.01). CONCLUSIONS: This study presents preliminary evidence that a rehabilitation programme that targets functional needs rather than diagnosis may be useful in improving function after traumatic brain injury.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".