ArticleBMJ open2026
Prospective validation of an AI algorithm to identify adult-onset type 1 diabetes misclassification: protocol for a non-interventional multicentre study.
Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Artificial intelligence-driven therapeutics for disease modification in type 1 diabetes: a digital public health and clinical translation framework.Frontiers in public health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAdult-onset type 1 diabetes (T1D) is often misclassified as type 2 diabetes (T2D), resulting in delayed treatment, missed opportunities for referrals to specialists and increased risk of complications including diabetic ketoacidosis. An electronic medical record (EMR)-based algorithm-originally trained on a large national EMR dataset to identify likely misclassified adult-onset T1D cases-was tested and retrained on a health information exchange (HIE) dataset from HealthShare Exchange (HSX). Promising results were achieved on historical data, particularly when using the retrained algorithm. However, its prospective validation is essential to more reliably assess its clinical utility and real-world precision in flagging high-risk patients for clinician review. METHODS AND ANALYSIS: This is a prospective, multicentre, non-interventional cohort study in two HSX-member healthcare organisations (HCOs) in southeastern Pennsylvania. At the onset of the study, all adult T2D patients are scored by the algorithm analysing HIE data on relevant predictors found in the 24-month lookback period. Patients meeting a prespecified score threshold estimated in retrospective testing to yield 10% recall will be presented to designated endocrinology or primary care providers for structured chart review, attribution confirmation and guideline-concordant follow-up (including autoantibody testing where appropriate). The primary endpoint is positive predictive value for confirmed adult-onset T1D among flagged patients. Secondary endpoints characterise operational cascade metrics (attribution, provider recommendation, test ordering/results and diagnosis updates) along with 95% CIs. Exploratory endpoints will assess provider adoption, interpretability and workflow integration via structured provider interviews. ETHICS AND DISSEMINATION: This study was reviewed and approved by Advarra Institutional Review Board (protocol Pro00075945). The Institutional Review Board waived patient informed consent and granted a full waiver of HIPAA authorisation for patient records, while providers were required to provide written informed consent. HSX data were accessed and shared under its member-defined use cases. Findings will be disseminated via peer-reviewed publications and conference presentations. Reporting will follow Strengthening the Reporting of Observational Studies in Epidemiology guidance for cohort studies.
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.