Use of a driving simulator to improve on‐road driving performance and cognition in persons with <scp>P</scp>arkinson's disease: A pilot study
Bibliographic record
Abstract
BACKGROUND/AIM: The use of simulators as an assessment and intervention tool for driving is an emerging field in occupational therapy. We investigated the potential usefulness of a driving simulator to improve on-road skills and cognitive functions in drivers with Parkinson's disease (PD). METHOD: Fifteen participants with PD, and Hoehn and Yahr stages between 2 and 3 participated in this pre-post comparison study. Twelve of the 15 individuals (median age (Q1-Q3), 68 (63.5-72.5); 10 men) completed 10 hours of training in a high-fidelity driving simulator. A practical road test as well as off-road cognitive and simulator tests were administered at pre-training and post-training. RESULTS: Nine participants, who passed the road test before training, passed at post-training. Furthermore, all three participants who initially failed the on-road test passed after training. Participants' performance improved significantly from pre- to post-training on two cognitive tests: (i) the Montreal Cognitive Assessment and (ii) Dot Cancellation test. CONCLUSION: This pilot study demonstrates the potential usefulness of a simulator to improve on-road driving and driving-related cognitive skills in PD. Adequately powered randomized controlled trials are needed to further expand this field of study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".