Caregivers’ Impressions Predicting Fitness to Drive in Persons With Parkinson’s
Bibliographic record
Abstract
Parkinson's disease (PD) is a common neurodegenerative disease, increasing in incidence, with a known impact on fitness to drive. Although great progress has been made on evidence-based guidelines for assessing fitness to drive of persons with PD, a need remains for early identification of at-risk drivers in need of comprehensive assessment. This study investigated whether caregivers of drivers with PD could predict the driver's on-road outcome. We also investigated whether the predictive value of their impressions differed from that of drivers themselves, their neurologist, or from information provided by standardized measures of visual and divided attention. Caregivers' risk impressions (odds ratio [OR] = 13.76, p = .03) and Trail Making Test Part B (Trails B; OR = 0.41, p = .02) emerged as significant predictors of passing an on-road assessment. Our findings suggest that caregiver impressions, with a measure of set shifting, may be used as an efficient screen to identify drivers with PD who are potentially at risk for failing an on-road assessment.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".