Driving assessment and rehabilitation using a driving simulator in individuals with traumatic brain injury: A scoping review
Bibliographic record
Abstract
BACKGROUND: Due to the heterogeneity of the lesion following a traumatic brain injury (TBI) and the complexity of the driving task, driving assessment and rehabilitation in TBI individuals is challenging. Conventional driving assessment (on-road and in-clinic evaluations) has failed demonstrating effectiveness to assess fitness to drive in TBI individuals. OBJECTIVE: We aimed to determine if driving simulators represent an interesting opportunity in assessing and rehabilitating driving skills in TBI individuals. METHODS: We searched PubMed, CINAHL and Cochrane library databases between 27-02-2014 and 08-04-2014 for articles published since 2000 with the contents of simulator driving assessment and rehabilitation. RESULTS: Out of 488, eight articles with the subject of simulator driving assessment and two with the subject of simulator driving rehabilitation in individuals with TBI were reviewed. CONCLUSIONS: Driving simulators represent a promising avenue for the assessment and rehabilitation of driving skills in TBI individuals as it allows control of stimuli in a safe, challenging and ecologically valid environment and offer the opportunity to measure and record driving performance. Additional studies, however, are needed to document strengths and limitations of this method.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".