Visual Attention Cut Points for Driver Fitness in Parkinson’s Disease
Bibliographic record
Abstract
This study determined whether the Useful Field of View™ Risk Index (UFOV RI) adds value as a predictor of on-road outcomes in drivers with Parkinson’s disease (PD) when considered with age, gender, and disease severity and compared with community-dwelling older drivers (Controls). A total of 101 PD drivers and 138 Controls underwent a comprehensive driving evaluation, including an on-road assessment. Logistic regression analyses determined the associations of age, gender, visual attention, and disease severity to on-road outcomes. Receiver operating characteristic curve analyses determined the optimal UFOV RI cut points to predict on-road outcomes. Above adding age and gender, the UFOV RI alone predicted on-road outcomes in PD, while the UFOV RI and age predicted on-road outcomes in Controls. Regardless of disease severity, visual attention was more impaired in PD than in Controls. The UFOV RI cut point of 3 provided the fewest misclassifications ( n = 25) in PD. The UFOV RI is a valid screening predictor of on-road outcomes across PD drivers of different disease severity, but has moderate sensitivity and specificity.
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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 | 0.001 | 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".