Driving behaviour and visual compensation in glaucoma patients: Evaluation on a driving simulator
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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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 | 0.001 |
| 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 | 0.000 | 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".