Glaucoma and On-Road Driving Performance
Bibliographic record
Abstract
PURPOSE: To investigate the on-road driving performance of patients with glaucoma. METHODS: The sample comprised 20 patients with glaucoma and 20 subjects with normal vision, all licensed drivers, matched for age and sex. Driving performance was tested over a 10-km route incorporating 55 standardized maneuvers and skills through residential and business districts of Halifax, Nova Scotia, Canada. Testing was conducted by a professional driving instructor and assessed by an occupational therapist certified in driver rehabilitation, masked to participant group membership and level of vision. Main outcome measures were total number of satisfactory maneuvers and skills, overall rating, and incidence of at-fault critical interventions (application of the dual brake and/or steering override by the driving instructor to prevent a potentially unsafe maneuver). Measures of visual function included visual acuity, contrast sensitivity, and visual fields (Humphrey Field Analyzer; Carl Zeiss Meditec, Inc., Dublin, CA; mean deviation [MD] and binocular Esterman points). RESULTS: There was no significant difference between patients with glaucoma (mean MD = -1.7 dB [SD 2.2] and -6.5 dB [SD 4.9], better and worse eyes, respectively) and control subjects in total satisfactory maneuvers and skills (P = 0.65), or overall rating (P = 0.60). However, 12 (60%) patients with glaucoma had one or more at-fault critical interventions, compared with 4 (20%) control subjects (odds ratio = 6.00, 95% CI, 1.46-24.69; higher still after adjustment for age, sex, medications and driving exposure), the predominant reason being failure to see and yield to a pedestrian. In the glaucoma group, worse-eye MD was associated with the overall rating of driving (r = 0.66, P = 0.002). CONCLUSIONS: This sample of patients with glaucoma with slight to moderate visual field impairment performed many real-world driving maneuvers safely. However, they were six times as likely as subjects with normal vision to have a driving instructor intervene for reasons suggesting difficulty with detection of peripheral obstacles and hazards and reaction to unexpected events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.002 | 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".