Driving Rehabilitation for Military Personnel Recovering From Traumatic Brain Injury Using Virtual Reality Driving Simulation: A Feasibility Study
Bibliographic record
Abstract
OBJECTIVE: To investigate the feasibility of virtual reality driving simulation rehabilitation training (VRDSRT) with military personnel recovering from traumatic brain injury (TBI). METHODS: Eleven men with TBI were randomly assigned as controls (n = 5) receiving residential rehabilitation only or the VRDSRT group (n = 6) receiving residential rehabilitation and VRDSRT. All subjects underwent pre- and post-assessments including simulator driving, and completing road rage and risky driving questionnaires. Between assessments, VRDSRT subjects received 4-6, 60- to 90-min rehabilitation training sessions involving practicing progressively more complex driving skills (lane position, speed control, etc.) through progressively more demanding traffic. RESULTS: VRDSRT was well received, considered realistic and effective, with no reported simulation sickness. Driving performance improved significantly in the VRDSRT group only (p < 0.01). They also demonstrated a reduction in road rage (p = 0.01) and risky driving (p = 0.04) at post-assessment. CONCLUSION: VRDSRT showed promising results with respect to retraining driving performance and behavior among military personnel recovering from 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".