ArticleTranslational vision science & technology2026
Identification of Risk Factors for Glaucoma Progression in Free-Text Clinical Notes Using a Local Large Language Model.
Article in Translational vision science & technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Automated Review of Patient Records: Privacy-Preserving Large Language Models for Identifying Incident Nonarteritic Anterior Ischemic Optic Neuropathy at Scale.Ophthalmology science · 2026Article
- Assessment of Large Language Models in Colorectal Cancer Multidisciplinary Tumor Board Decision-Making: A Retrospective Single-Center Comparison of Guideline-Integrated General-Purpose vs. Domain-Specialized Models.Current oncology (Toronto, Ont.) · 2026Article
Corrections and comments
- Update of
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To evaluate the performance of a large language model (LLM) in identifying medication non-adherence, visit non-adherence, and family history of glaucoma (FHoG) in clinical notes from the electronic health record (EHR). Methods: We extracted clinical notes of 1250 glaucoma-related encounters between 2014 and 2024 and structured EHR family history field data from the Bascom Palmer Ophthalmic Repository; of these, 125 randomly selected notes (10%) were used for prompt development and were excluded from analysis. Two fellowship-trained glaucoma specialists labeled notes for evidence of non-adherence and FHoG, which was used as the reference standard. We utilized MedGemma-27B-text-it, a specialized medical LLM, to identify medication non-adherence, visit non-adherence, and FHoG. We calculated accuracy, sensitivity, and specificity of LLM performance for each task, the Jaccard index for FHoG, and the mean squared error (MSE) of number of family members with glaucoma. Results: Prevalence of medication non-adherence, visit non-adherence, and FHoG were 7.3%, 4.7%, and 29.2%, respectively. LLM accuracy was 0.91 (sensitivity, 0.96; specificity, 0.91) for medication non-adherence and 0.96 (sensitivity, 0.97; specificity, 0.94) for visit non-adherence. For FHoG, LLM accuracy was 0.98 (sensitivity, 0.99; specificity, 0.99) with a Jaccard index of 0.99, whereas EHR family history field accuracy and Jaccard index were 0.49 and 0.75, respectively. LLM and EHR MSE in quantifying the number of relatives with glaucoma were 0.05 ± 0.56 and 0.85 ± 1.80, respectively (P < 0.001). Conclusions: LLMs identified non-adherence to medication and visit schedules, as well as degree of FHoG, in clinical notes with high accuracy. Translational Relevance: Local LLM pipelines can enable large-scale research into glaucoma risk factors that are unavailable in discrete EHR fields.
Indexed as
Identifiers
What OpenQuestion holds
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.