Training driving ability in a traumatic brain-injured individual using a driving simulator: a case report
Bibliographic record
Abstract
BACKGROUND: Traumatic brain injury (TBI) causes functional deficits that may significantly interfere with numerous activities of daily living such as driving. We report the case of a 20-year-old woman having lost her driver's license after sustaining a moderate TBI. OBJECTIVE: We aimed to evaluate the effectiveness of an in-simulator training program with automated feedback on driving performance in a TBI individual. METHODS: The participant underwent an initial and a final in-simulator driving assessment and 11 in-simulator training sessions with driving-specific automated feedbacks. Driving performance (simulation duration, speed regulation and lateral positioning) was measured in the driving simulator. RESULTS: Speeding duration decreased during training sessions from 1.50 ± 0.80 min (4.16 ± 2.22%) to 0.45 ± 0.15 min (0.44 ± 0.42%) but returned to initial duration after removal of feedbacks for the final assessment. Proper lateral positioning improved with training and was maintained at the final assessment. Time spent in an incorrect lateral position decreased from 18.85 min (53.61%) in the initial assessment to 1.51 min (4.64%) on the final assessment. CONCLUSION: Driving simulators represent an interesting therapeutic avenue. Considerable research efforts are needed to confirm the effectiveness of this method for driving rehabilitation of individuals who have sustained a 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".