ArticleAtencion primaria
[Assessment of the use of retinography as a screening method for the early diagnosis of chronic glaucoma in Primary Care: Validation for screening in populations with open-angle glaucoma risk factors].
Article in Atencion primaria. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04132401 (Artificial Intelligence for the Detection of Central Retinal Disease and Non-mydriatic Glaucoma in the Context of Patients With Diabetes Mellitus in Primary Care), which is not on this map. Cited by 3 papers.
What it found
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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.
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.
Artificial Intelligence for the Detection of Central Retinal Disease and Non-mydriatic Glaucoma in the Context of Patients With Diabetes Mellitus in Primary Care: A Prospective Study Comparing the Diagnostic Capacity of an AI Algorithm
Who cites it
3 citing papers in PubMed, 7 citations in OpenAlex.
- Clinical validation of artificial intelligence algorithms for the detection of different central-involved retinal pathologies and glaucoma from non-mydriatic images.Frontiers in artificial intelligence · 2026Trial
- Smartphone-Assisted Glaucoma Screening in Patients With Type 2 Diabetes: a Pilot Study.Medical hypothesis, discovery & innovation ophthalmology journal · 2020Article
- Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care: Protocol for Algorithm Development.JMIR research protocols · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe aim of this study is to determine usefulness, validity of retinographies performed in Primary Care as a tool for early diagnosis of open-angle chronic glaucoma (OAG).
designAn observational, descriptive and cross-sectional study with two blinded parallel observers: 2 general practitioners and 1 ophthalmologist. LOCATION: Urban Primary Care Health Centre, and the Ophthalmology Department outpatient clinic.
participantsA total of 196 patients of both genders, between 40-70years, with diabetes and hypertension, and undiagnosed with glaucoma, were recruited by phone call after checking patient lists. Two patients that did not arrive for their appointments for the ophthalmology tests were considered as losses. MEASUREMENTS: For the quantitative diagnostic variable of glaucoma: Accuracy calculated from the sensitivity and specificity, safety from the positive and negative predictive values and the positive and negative probability ratio; interobserver concordance by Kappa index ratio and the intraclass correlation (IC).
resultsThe retinography for OAG screening has a sensitivity of 21% (95%CI: 0-43%), a specificity of 93% (95%CI: 89-97%), a negative predictive value of 94% (95%CI: 90-97%), and positive of 20% (95%CI: 0-40%); positive probability ratio of 3.07 (95%CI: 0.98-9.62) and negative 0.84 (95%CI: 0.64-1.11). The IC was 0.653 (95%CI: 0.495-0.769) and kappa index of 0.140 (0.106ET).
conclusionsAccording to this proposed model, retinography is not a useful tool for the early diagnosis of OAG in Primary Care, as it is not safe enough. Before it can be used, it would need adjustments for its low sensitivity, and the use other combined tests. The training of general practitioners would also need to be improved.
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Registered trials
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 OpenQuestion graph.