ReviewCureus2025
Machine Learning for Predicting the Transition From Gestational Diabetes to Type 2 Diabetes: A Systematic Review.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled 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.
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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of AI in Predicting the Progression of Gestational Diabetes to Type 2 Diabetes: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Postpartum GLP-1 receptor agonists and SGLT-2 therapies in women with prior gestational diabetes: current evidence and uncertainties.BMC medicine · 2026Review
- Traditional statistics and artificial intelligence-based prognostic models for predicting type 2 diabetes mellitus after gestational diabetes: a systematic review.Diagnostic and prognostic research · 2026Review
- Machine Learning-Derived Predictive Risk Score for Prediabetes and Type 2 Diabetes Development in Parous Women.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gestational diabetes mellitus (GDM) significantly increases the risk of developing type 2 diabetes (T2D) postpartum. Early identification of high-risk women using machine learning (ML) models could enable targeted interventions and improve outcomes. This systematic review aims to evaluate the performance, predictive features, and methodological quality of ML models designed to predict the transition from GDM to T2D. A comprehensive search was conducted across PubMed, Scopus, IEEE Xplore, and Web of Science, yielding 178 records. After removing duplicates and screening for eligibility, 13 studies were included. Data on study characteristics, ML algorithms, predictive features, model performance, and validation methods were extracted. Risk of bias was assessed using the PROBAST (Prediction model Risk of Bias Assessment Tool). The included studies demonstrated variable performance, with area under the curve (AUC) values ranging from 0.72 to 0.92. Models incorporating omics data outperformed clinical-only models. Key predictive features included age,
Indexed as
Identifiers
What Socratic 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 Socratic graph.