Predicting On‐Road Driving Performance and Safety in Cognitively Impaired Older Adults
Bibliographic record
Abstract
OBJECTIVES: To evaluate the ability to predict on-road driving in cognitively impaired older drivers. DESIGN: Cross-sectional observational study. SETTING: Laboratory tests and on-road assessment. PARTICIPANTS: Drivers with cognitive impairment (Mini-Mental State Examination score < 26, N = 43, mean age 74). MEASUREMENTS: The Roadwise Review, a hazard perception test (HPT), several vision tests, and a standardized 18-km driving assessment. RESULTS: The best prediction of passing or failing the on-road test was a combination of the HPT, leg strength, visual acuity, visual search and working memory, and number of medications taken (Nagelkerke coefficient of determination = 0.40). The sensitivity of the model was 71%, and the specificity was 75%. CONCLUSION: Further research is required to determine how these tests may be used or combined with other data (e.g., medical history) to assess fitness to drive of cognitively impaired older drivers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".