Social Determinants of Health and Glaucoma Screening and Detection in the SIGHT Studies
Bibliographic record
Abstract
PRÉCIS: Targeted glaucoma screenings in populations with high levels of poverty and high proportions of people who identify as African American or Hispanic/Latino identified a 27% rate of glaucoma and suspected glaucoma, which is 3 times the national average. PURPOSE: To describe the neighborhood-level social risk factors across the 3 SIGHT Study sites and assess potential characteristics of these populations to help other researchers effectively design and implement targeted glaucoma community-based screening and follow-up programs in high-risk groups. METHODS/RESULTS: In 2019, Columbia University, the University of Michigan, and the University of Alabama at Birmingham each received 5 years of CDC funding to test a wide spectrum of targeted telehealth delivery methods to detect glaucoma in community-based health delivery settings among high-risk populations. This collaborative initiative supported innovative strategies to better engage populations most at risk and least likely to have access to eye care to detect and manage glaucoma and other eye diseases in community-based settings. Among the initial 2379 participants enrolled in all 3 SIGHT Studies; 27% screened positive for glaucoma/glaucoma suspect. Of all SIGHT Study participants, 91% were 40 years of age and older, 64% identified as female, 60% identified as African-American, 32% identified as White, 19% identified as Hispanic/Latino, 53% had a high school education or less, 15% had no health insurance, and 38% had Medicaid insurance. Targeted glaucoma screenings in populations with high levels of poverty and high proportions of people who identify as African American or Hispanic/Latino identified a 27% rate of glaucoma and suspected glaucoma, three times the national average. CONCLUSION: These findings were consistent across each of the SIGHT Studies, which are located in 3 geographically distinct US locations in rural Alabama, small urban locations in Michigan, and urban New York City.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".