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 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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".