A randomized clinical trial to determine effectiveness of driving simulator retraining on the driving performance of clients with neurological impairment
Bibliographic record
Abstract
Introduction Following a neurological event, returning to driving is an important activity contributing to improved participation within the community. The purpose of this study was to examine the effectiveness of driving simulator retraining on driving in clients with neurological impairment and to examine factors associated with treatment effectiveness. Method Individuals with non-degenerative brain injury were randomized to either a simulator training or control group. The simulator group received individualized training (16 sessions) using a driving simulator. The control group did not receive intervention. A blind evaluator assessed participants on the DriveAble On-Road Driving Evaluation. Results There was no significant difference between groups in the proportion of individuals who passed the driving evaluation (Chi 2 = 0.65; p = 0.42; CI = −0.41 to +0.17). However, participants with moderate impairment who received simulator training were more likely to pass the driving test compared with those in the control group (86% versus 17%; Chi 2 = 6.2; p = 0.03; CI = −1.00 to −0.30). There were no differences in pass rate according to diagnosis, gender, or for those with severe impairments. Conclusion Results provide clinicians with preliminary information on the potential clinical usefulness of driving simulator training. While the findings do not support simulator retraining for the group as a whole, they suggest that clients with moderate impairment have the potential to benefit.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".