Predictors of handicap situations following post-stroke rehabilitation
Bibliographic record
Abstract
PURPOSE: Many stroke survivors have to cope with impairments and disabilities that may result in the occurrence of handicap situations. The purpose of the study was to explore bio-psycho-social predictors of handicap situations six months after discharge from an intensive rehabilitation programme. METHODS: At discharge from a rehabilitation programme, participants were evaluated with instruments measuring motor, sensory, cognitive, perceptual, affective and psychosocial impairments and disabilities that may play a role in the development of handicap. Some other demographic and clinical variables, and those related to rehabilitation, were also collected. Six months later, they were re-assessed in their own environment in order to document their handicap level with the Assessment of Life Habits (LIFE-H). RESULTS: One hundred and thirty-two stroke patients participated in the discharge evaluation and 102 of them also participated in the handicap measurement. Relationships between handicap level and impairments and disabilities were all statistically significant. Multiple regression analyses indicated that affect, lower extremity co-ordination, length of stay in rehabilitation, balance, age and comorbidity at the end of an intensive rehabilitation programme are the best predictors of handicap situations six months later (adjusted R(2): 68.1%). CONCLUSIONS: In spite of its exploratory nature, this study revealed that, among a substantial number of personal characteristics, some were more related to a handicap measure and have greater predictive value. Other studies should be carried out to validate these findings and to consider more environmental factors in order to better understand factors related to the development of handicap situations.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".