Risk of Falls and Motor Vehicle Collisions in Glaucoma
Bibliographic record
Abstract
PURPOSE: To investigate the risk of falls and motor vehicle collisions (MVCs) in patients with glaucoma. METHODS: The sample comprised 48 patients with glaucoma (mean visual field mean deviation [MD] in the better eye = -3.9 dB; 5.1 dB SD) and 47 age-matched normal control subjects, who were recruited from a university-based hospital eye care clinic and are enrolled in an ongoing prospective study of risk factors for falls, risk factors for MVCs, and on-road driving performance in glaucoma. Main outcome measures at baseline were previous self-reported falls and MVCs, and police-reported MVCs. Demographic and medical data were obtained. In addition, functional independence in daily living, physical activity level and balance were assessed. Clinical vision measures included visual acuity, contrast sensitivity, standard automated perimetry, useful field of view (UFOV), and stereopsis. Analyses of falls and MVCs were adjusted to account for the possible confounding effects of demographic characteristics, medications, and visual field impairment. MVC analyses were also adjusted for kilometers driven per week. RESULTS: There were no significant differences between patients with glaucoma and control subjects with respect to number of systemic medical conditions, body mass index, functional independence, and physical activity level (P > 0.10). At baseline, 40 (83%) patients with glaucoma and 44 (94%) control subjects were driving. Compared with control subjects, patients with glaucoma were over three times more likely to have fallen in the previous year (odds ratio [OR](adjusted) = 3.71; 95% CI, 1.14-12.05), over six times more likely to have been involved in one or more MVCs in the previous 5 years (OR(adjusted) = 6.62; 95% CI, 1.40-31.23), and more likely to have been at fault (OR(adjusted) = 12.44; 95% CI, 1.08-143.99). The strongest risk factor for MVCs in patients with glaucoma was impaired UFOV selective attention (OR(adjusted) = 10.29; 95% CI, 1.10-96.62; for selective attention >350 ms compared with </=350 ms). CONCLUSIONS: There is an increased risk of falls and MVCs in patients with glaucoma.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".