Feasibility of a Bi-national Consulate-based Teleophthalmology Screening and Referral Protocol for Diabetic Retinopathy among Mexican Migrants in the United States
Bibliographic record
Abstract
AIMS: To evaluate a bi-national consulate-based teleophthalmology screening service for diabetic retinopathy (DR) among Mexican migrants in the U.S. METHODS: Adult visitors (n=508) at Mexican consulates in California with self-reported diabetes underwent questionnaires and fundus photography. Photographs were graded for DR by retina fellows in Mexico via teleophthalmology. Participants were contacted with results and provided referrals when necessary. RESULTS: Nearly all (97.6%) participants were aware that diabetes can cause vision loss. One-quarter (24.4%) had undergone an eye examination in the past year. Barriers to care were cost (53.9%) and insurance (45.6%). Most (85.4-91.1%) reported that Spanish-speaking providers and provision of screening in primary care would increase participation in screening. Any DR, vision-threatening DR, or proliferative DR were found in 30.2%, 9.9%, and 5.4% of participants, respectively. Nearly one-fifth (19.5%) received referrals. CONCLUSIONS: Screening in Mexican consulates may improve DR detection and treatment among Mexican migrants in the U.S.
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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 | 0.028 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".