Postural/Gait and Cognitive Function as Predictors of Driving Performance in Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: The primary influence of motor symptoms on driving performance remains unclear due to the inconsistent use of various motor rating scales used in prior studies. OBJECTIVE: This study aimed to determine which of three measures utilized in PD, the Unified Parkinson's Disease Rating Scale (UPDRS) motor section; the Modified Hoehn and Yahr; and the Rapid Paced Walk Test would best predict pass/fail outcomes on a road test in a sample of PD drivers. METHODS: All participants (N = 55; 79% men) completed a road test. Receiver Operating Characteristics were then contrasted for all subjects based on assessments from all three disease severity indices. MMSE scores were then modelled with significant disease severity measures (if any) to determine if the predictive accuracy could be improved. RESULTS: The Rapid Paced Walk Test and the Modified Hoehn & Yahr both predicted pass/fail outcomes on the road test (Area under the curve of 0.73 and 0.82, respectively). UPDRS motor scores, however, did not predict safe driving. When optimal cut-off points on the Modified Hoehn & Yahr (≥ 2.5) and Rapid Paced Walk Test (>6.22 seconds) were modelled with MMSE scores indicative of mild cognitive impairment (<27), the model accurately classified 92% and 100% as failing the road test, respectively. CONCLUSION: Although the Rapid Paced Walk Test had a slight advantage in differentiating between pass/fail outcomes compared to the Modified Hoehn & Yahr, both tests alone cannot be used in isolation to predict driving safety. Predictive accuracy can be improved using both select cut-off points on the Modified Hoehn & Yahr and Rapid Paced Walk test with MMSE scores in PD drivers. Though these findings are useful, an on-road test is still the gold standard, and screening should always be followed by formal testing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".