Driving with Parkinson’s disease: Cut points for clinical predictors of on-road outcomes
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disorder that impacts a person's fitness to drive. Practitioners require a sensitive and predictive battery of clinical tests to identify at-risk drivers. PURPOSE: This study aimed to identify clinical predictors and their optimal cut points, sensitivity, specificity, and predictive values of on-road outcomes in drivers with PD. METHOD: Participants ( N = 101) underwent a comprehensive driving evaluation. We identified predictors of pass/fail outcomes through logistic regression and computed optimal cut points through receiver operating characteristic curves and corresponding Youden indexes. FINDINGS: The Trail Making Test Part B (Trails B; sensitivity = .89, specificity = .74; positive predictive value [PPV] = .71; negative predictive value [NPV] = .91) and contrast sensitivity (sensitivity = .82, specificity = .63; PPV = .61; NPV = .84) emerged as significant predictors. The optimal cut point for the Trails B was 108 s (area under the curve = .86). IMPLICATIONS: Occupational therapists can benefit from implementing Trails B and contrast sensitivity screening as part of in-office screening of potentially at-risk drivers with PD.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".