Which Factors Influence Functional Patients Improvements During Rehabilitation?
Bibliographic record
Abstract
BACKGROUND: Rehabilitation in patients with disabilities is an important aspect of tertiary prevention. Severity of disability, evaluated by global measures of autonomy, are essential for functional outcome evaluation. AIM: To determine the effectiveness of a rehabilitation programme in terms of percentage functional improvement (PFI); to verify the role of gender, age and length of stay (LOS), by motor and cognitive domains, on PFI. DESIGN: longitudinal study. SETTING: An intensive rehabilitation hospital. POPULATION: 305 inpatients. METHODS: The disability has been investigated using the Functional Independence Measure (FIM). Percentage differences between discharge and admission were calculated for FIM score. Wilcoxon matched pair test for the six areas and the two domains of the FIM score were calculated. The effect of LOS, gender and age on PFI were studied with Robust regression. RESULTS: Neurological and Orthopaedic patients had improvements on Motor and Cognitive domains. The greatest gains were in the Self Care, Sphintere Control, Transfer and Locomotion Areas (p ≤ 0.001). LOS was associated (p < 0.001) with PFI while age resulted borderline significant (p=0.049) in the cognitive domain in Neurological patients. CONCLUSION: The rehabilitation improved the overall conditions of neurological and orthopaedic patients. LOS emerged as the most important determinant in PFI.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".