Functional independence measure at rehabilitation admission as a predictor of return to driving after traumatic brain injury
Bibliographic record
Abstract
OBJECTIVE: To examine the utility of three common measures as predictors of return to driving after traumatic brain injury (TBI): Glasgow Coma Score (GCS) within the first 24 hours of injury and both Functional Independence Measure (FIM) and Disability Rating Scale (DRS) at rehabilitation admission. METHODS: Seventy-two participants with TBI completed a questionnaire that assessed return to driving post-TBI, as measured by reinstatement of the driver's license. Participants who did not return to driving for non-medical reasons or who had not driven pre-injury and did not obtain a driver's license post-injury were excluded from analysis. This produced a final sample of 59 participants. Scores on GCS, FIM and DRS, leveraged from an existing database, were compared between participants who had and those who had not returned to driving post-injury. Multiple logistic regression analysis was performed to determine the relationship of each predictor variable to return to driving. RESULTS: Only the FIM score at rehabilitation admission was significantly associated with return to driving (p < 0.01). FIM score had a sensitivity of 72% and specificity of 73% with respect to return to driving. CONCLUSIONS: This study supports the use of FIM at rehabilitation admission as a predictor of return to driving. Future studies should be directed at identifying other measures to be used in combination with FIM to accurately predict return to driving post-TBI.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".