Glaucoma Screening and Referral Risk Factors in a High-Risk Population: Follow-Up Study of the Manhattan Vision Screening Study
Bibliographic record
Abstract
PRÉCIS: Community-based eye health screenings that incorporated fundus photography and optometric exams in a high-risk NYC population effectively identified a higher than average number of participants that required an in-office glaucoma evaluation. PURPOSE: To report glaucoma screening rates and risk factors associated with referral for in-office glaucoma evaluation in the Manhattan Vision Screening and Follow-up Study (NYC-SIGHT). METHODS: In this 5-year, cluster-randomized clinical trial, eligible individuals aged 40 and older were recruited from affordable housing developments and senior centers. Visual acuity with correction, intraocular pressure (IOP) measurements, and nonmydriatic fundus photography were conducted. Images were graded by a glaucoma specialist; those with an abnormal image were referred; those who failed the screening or had an unreadable fundus image were examined by the study optometrist. χ 2 tests and stepwise multivariate logistic regression analyses were conducted to determine factors associated with glaucoma referral. RESULTS: Totally, 708 participants were screened; 189 (26.6%) were referred for an in-office glaucoma evaluation due to an abnormal optic disc image (n=138) or abnormal optometric exam (n=51). Those referred had a mean age 68.5±11.7 years and were 60% female, 57% Black, and 37% Hispanic. Stepwise multivariate logistic regression showed participants with self-reported glaucoma (OR: 8.096, 95% CI: 4.706-13.928, P =0.000), IOP > 23 mm Hg at the screening (OR: 3.944, 95% CI: 1.704-9.128, P =0.001), or wore prescription eyeglasses (OR: 1.601, 95% CI: 1.034-2.48, P =0.035) had higher odds of being referred for an in-office glaucoma evaluation. Of those referred, 106 (56%) attended, 36 participants (34%) were diagnosed with glaucoma and 38 participants (35.8%) as glaucoma suspects. CONCLUSION: Our findings support public health approaches that focus on community-based eye health screenings in high-risk populations and prioritize underserved communities.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".