Evidence map›Paper›PMID 40924935›Full record

Observational studyDiabetes care2025

Quantifying Barriers to Diabetic Eye Screening: A Two-Center Study at the University of California.

Aryan Ayati, Shadera Azzam, Stella Ko, Cobi Ben-David, Michelle Wang, Nicole Bonine, David Tabano, Nina Malik, Frank Brodie, Mitul C Mehta and 1 more

Abstract readMulticenter StudyObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Diabetes care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Aryan AyatiBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.ORCID 0000-0002-0241-6865
Shadera AzzamBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.
Stella KoGenentech, Inc., South San Francisco, CA.
Cobi Ben-DavidGavin Herbert Eye Institute, University of California, Irvine, Irvine, CA.
Michelle WangBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.ORCID 0000-0002-3207-1490
Nicole BonineGenentech, Inc., South San Francisco, CA.
David TabanoGenentech, Inc., South San Francisco, CA.
Nina MalikGenentech, Inc., South San Francisco, CA.
Frank BrodieDepartment of Ophthalmology, University of California, San Francisco, San Francisco, CA.
Mitul C MehtaGavin Herbert Eye Institute, University of California, Irvine, Irvine, CA.
Vivek A RudrapatnaBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.ORCID 0000-0003-1789-3004

Funding

A data science framework for transforming electronic health records into real-world evidenceR00LM014099 · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2025 to 2025
$249k
Genentech IncNLM NIH HHS R00 LM014099
6 · The paper itself

Abstract

objectiveThis study aimed to evaluate the diabetic eye disease screening continuum at two academic centers and identify its barriers. RESEARCH DESIGN AND

methodsWe analyzed health records from the University of California, San Francisco, and University of California, Irvine, to identify primary care patients needing diabetic eye screening. We tracked referrals, screenings, diagnoses, and treatments to evaluate predictors and the impact of an automated referral system. We analyzed physician notes using GPT-4o to determine reasons for missed screenings.

resultsOf 8,240 unscreened patients with type 2 diabetes mellitus (T2DM), 43% received a referral, and only 16% completed screening within 1 year. Demographic, provider, and socioeconomic factors predicted adherence, with referrals being the strongest predictor. An automated referral system could improve screening rates to 22-34%. Clinician notes cited comorbidities, scheduling challenges, logistical issues, coronavirus disease 2019, and personal circumstances as barriers.

conclusionsMany patients with T2DM remain unscreened after primary care visits. Although an automated referral system may partially improve adherence, additional tailored strategies are needed.

Indexed as

Diabetes Mellitus, Type 2Diabetic RetinopathyMass ScreeningAgedCaliforniaFemaleHumansMaleMiddle AgedPrimary Health CareReferral and Consultation

Identifiers

PMID40924935
PMCPMC12583403

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.