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Record W4213204785 · doi:10.1353/hpu.2022.0017

Feasibility of a Bi-national Consulate-based Teleophthalmology Screening and Referral Protocol for Diabetic Retinopathy among Mexican Migrants in the United States

2022· article· en· W4213204785 on OpenAlexaboutno aff
Joshua Foreman, Maureen Lahiff, Enrique Graue Hernández, Aída Jiménez-Corona, Rodrigo Matsui, Jorge Cuadros, Stephany Pizano, Hannah Peters, Xóchitl Castañeda, Marlon Maus

Bibliographic record

VenueJournal of Health Care for the Poor and Underserved · 2022
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsDiabetic retinopathyMedicineReferralFamily medicineOptometryDiabetes mellitusQuarter (Canadian coin)Fundus photographyFundus (uterus)Primary careGerontologyOphthalmologyVisual acuityGeographyFluorescein angiography

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.126
GPT teacher head0.406
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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