Glaucoma and mobility performance: the Salisbury Eye Evaluation Project.
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of glaucoma on mobility in a population-based cohort. DESIGN: Population-based observational study. PARTICIPANTS: Persons examined as part of a population-based eye disease study. METHODS: Subjects performed a series of tasks, including walking an obstacle course, climbing stairs, performing tandem stands, and walking a 4-meter course. Persons with glaucoma were compared with those without glaucoma to identify differences in mobility. MAIN OUTCOME MEASURES: Speed to complete an obstacle course, number of bumps, ability to perform tandem stands, and walking and stair climbing speed. RESULTS: One thousand two hundred fifty subjects participated in the study. In an analysis adjusting for age, race, and gender, walking speed through the obstacle course was 2.4 m/minute slower for persons with bilateral glaucoma, and these individuals experienced 1.65 times the number of bumps when compared with persons without glaucoma (P<0.05 for both). None of the associations was statistically significant comparing persons with unilateral glaucoma with normals. This association remained after adjusting for other potentially confounding factors including visual acuity (VA), body mass index, height, Mini-Mental State Examination score, grip strength, arthritis, depressive symptoms, comorbidities, and use of mobility aids. Additional analyses indicate that visual field loss drives this association. CONCLUSIONS: Bilateral glaucoma reduces mobility performance as measured in multiple ways in this population-based study of community-dwelling individuals. Persons with bilateral glaucoma completed the walking course more slowly and had more bumps even after adjusting for use of a mobility aid, comorbidities, and VA. After adjusting for all other factors, persons with bilateral glaucoma walked on average 2.4 m less per minute through the course than those without glaucoma.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".