Community integration following multidisciplinary rehabilitation for traumatic brain injury
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To determine the extent to which participation in a multidisciplinary rehabilitation programme and patient characteristics predict improvement in community integration following mild-to-moderate traumatic brain injury (TBI). RESEARCH DESIGN: A non-randomized case-control study was conducted employing a pre-test-post-test multiple regression design. METHODS AND PROCEDURES: Archival data for 42 patients with mild-to-moderate TBI who completed the Community Integration Questionnaire (CIQ) at intake and again 6-18 months later were analysed. Half the sample participated in an intensive outpatient rehabilitation programme that provided multi-modal interventions, while the other half received no rehabilitation. The two groups were matched on age, education and time since injury. RESULTS: On the CIQ Home Integration scale, participation in rehabilitation and female gender predicted better outcome. On the Productivity scale, patients with a lower age at injury had better outcome. Outcome on both of these scales, as well as on the Social Integration scale, was predicted by the baseline pre-test score (initial severity). CONCLUSIONS: Overall, multidisciplinary rehabilitation appeared to increase personal independence. It is also concluded that: (1) multivariate analysis can reveal the relative importance of multiple predictors of outcome; (2) different predictors may predict different aspects of outcome; and (3) more sensitive and specific outcome measures are needed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".