Clinical Assessments as Predictors of Primary On-Road Outcomes in Parkinson’s Disease
Bibliographic record
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder that affects fitness to drive. Research that has examined clinical predictors of fitness to drive in PD, using the on-road assessment as the gold standard, has generally used a dichotomous pass/fail decision. However, on-road assessments may also result in one of two additional outcomes (pass with recommendations, or fail-remediable). Individuals within these subgroups may benefit from interventions to improve their fitness to drive abilities. This study investigated clinical predictors that could be indicative of the pass, pass with recommendations, or fail-remediable categories for drivers with PD (N = 99). Trails B, Left Finger to Nose Test, and contrast sensitivity measures were identified as significant predictors for the pass, and pass with recommendations subgroups. No significant predictors were identified for the fail-remediable subgroup. Results from this study provide a foundation for clinicians to identify drivers who can benefit from recommendations to preserve their driving abilities.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".