Clinical Factors Predicting Voluntary Driving Cessation among Patients with Parkinson’s Disease
Bibliographic record
Abstract
Factors that influence the decision of voluntary driving cessation in patients living with Parkinson’s disease (PD) are still unclear. We aimed to reveal the factors affecting the decision of voluntary driving cessation in patients with PD. This hospital-based cross-sectional study recruited consecutive outpatients with PD. Data on sociodemographic and clinical characteristics and medication use were collected from the patients using semistructured interviews. Cognitive function was evaluated using the Japanese version of the Montreal Cognitive Assessment (MoCA-J). We excluded patients with dementia or motor impairment ( Hoehn − Yahr stage > 3 ). We divided the patients into two groups, with and without voluntary driving cessation (D: driver; RD: retired driver), and conducted investigations using multivariate logistic regression analyses. Of the 40 patients, 8 (20.0%) voluntarily retired from driving. Patients who decided on driving cessation had a higher prevalence of freezing of gait (FOG) (D vs. RD, 25.0% vs. 87.5%; P = 0.001 ) and tended to have lower scores for attention in the MoCA-J (D vs. RD, 5.0 ± 1.2 vs. 4.1 ± 1.4 ; P = 0.086 ). Multivariable analysis showed that FOG was independently associated with driving cessation (odds ratio: 14.46, 95% confidence interval: 1.91–303.74). FOG was associated with voluntary driving cessation in patients with PD without dementia or severe motor impairment. Physicians should consider providing extensive social support to maintain patients’ mobility and independence, especially if the patients have these clinical factors.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".