Driving behaviour and visual compensation in glaucoma patients: Evaluation on a driving simulator
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: To assess the driving performance and both the visual scanning and driving compensations of glaucoma patients. METHODS: In this case-control pilot study, the driving behaviour and performance of 14 patients with glaucoma and nine healthy age- and sex-similar control subjects were compared in a fixed-base driving simulator. All subjects performed in four scenarios with one to two hazardous situations on urban streets, for a total of five hazards. Measurements taken during the tests included reaction times, longitudinal regulation, lateral control and eye and head movements. RESULTS: Glaucoma patients showed poor driving performance with longer reaction time to hazardous situations than control subjects: pedestrians crossing the road from the left (p < 0.022) or from the right (p = 0.013), and vehicles coming from the left (p = 0.002). Their mean duration of lateral excursion was longer (p = 0.045), and they showed more lane excursions in a wide left curve (p = 0.045). Glaucoma patients also showed a higher standard deviation of time-headway (p = 0.048) with preceding vehicles. Analyses of driving behavioural compensations on curved roads showed that glaucoma patients stayed closer to the centre line in large (p = 0.006) and small (p = 0.025) left curves and on small right curves (p = 0.041). Additionally, on straight roads, as compared to control subjects, glaucoma patients showed longer mean time-headway (p = 0.032) and lower mean speed (p = 0.04). Finally, the glaucoma group exhibited a larger standard deviation of horizontal gaze (p = 0.034) than the control subjects. CONCLUSIONS: In a virtual driving environment, glaucoma patients exhibited unsafe driving behaviours, despite their driving and eye-scanning compensations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it