In-simulator training of driving abilities in a person with a traumatic brain injury
Bibliographic record
Abstract
AIM: This study reports the case of a 23-year-old woman (MC) who sustained a severe traumatic brain injury in 2004. After her accident, her driving license was revoked. Despite recovering normal neuropsychological functions in the following years, MC was unable to renew her license, failing four on-road evaluations assessing her fitness to drive. METHOD: In hope of an eventual license renewal, MC went through an in-simulator training programme in the laboratory in 2009. The training programme aimed at improving features of MC's driving behaviour that were identified as being problematic in prior on-road evaluations. To do so, proper driving behaviour was reinforced via driving-specific feedback provided during the training sessions. RESULTS: After 25 sessions in the simulator (over a period of 4 months), MC significantly improved various components of her driving. Notably, compared to early sessions, later ones were associated with a reduced cognitive load, less jerky speed profiles when stopping at intersections and better vehicle control and positioning. A 1-year retention test showed most of these improvements were consistent. CONCLUSIONS: The learning principles underlying well conducted simulator-based education programmes have a strong scientific basis. A simulator training programme like this one represents a promising avenue for driving rehabilitation. It allows individuals without a driving license to practice and improve their skills in a safe and realistic environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".