Early neuropsychological tests as correlates of return to driving after traumatic brain injury
Bibliographic record
Abstract
OBJECTIVE: To assess the ability of neuropsychometric tests administered during inpatient rehabilitation to predict return to driving after traumatic brain injury (TBI). DESIGN: Retrospective, matched case-controlled study. METHODS: Sixty-seven participants with TBI, drawn from an existing database, completed a questionnaire that assessed return to driving post-TBI, as measured by reinstatement of the driver's license. Drivers were individually case-matched to non-drivers on age, Glasgow Coma Scale (GCS), Disability Rating Scale (DRS) and the rehabilitation admission interval (RAI). Scores on four neuropsychological tests (Trail-Making A, Trail Making B, Digit Span-forward and Digit Span-backward), administered during the rehabilitation stay, were compared between case-matched drivers and non-drivers. OUTCOME MEASURE: Return to driving, as defined by reinstatement of the driver's license. RESULTS: Participants who had returned to driving were comparable to those who had not returned to driving with respect to demographic variables, initial injury severity and baseline functioning. Scores on two neuropsychological assessments were significantly better in participants who had returned to driving than in those who had not: Trail-making A (p < 0.01) and Trail-making B (p < 0.01). CONCLUSIONS: The results suggest that neuropsychological measures of processing speed and cognitive flexibility may predict return to driving after 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".