ArticleFrontiers in physiology2022
Unraveling the Factors Determining Development of Type 2 Diabetes in Women With a History of Gestational Diabetes Mellitus Through Machine-Learning Techniques.
Article in Frontiers in physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses 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
10 citing papers in PubMed, 2 syntheses or guidelines 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
- Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Glucose Metabolism and Modeling Approaches in Pregnancy: From Dynamic Metabolic Tests to Continuous Glucose Monitoring.Diabetes & metabolism journal · 2026Article
- 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
- De novo generation and in silico screening of anti-diabetic peptide candidates via a deep learning-attention framework with physicochemical feature fusion.Scientific reports · 2026Article
- Precision Medicine and Shared Decision-Making to Advance Gestational Diabetes Management: Genetic, Metabolic, and Gut Microbiota Factors.Patient preference and adherence · 2026Review
- Clinical guidelines for women with a history of gestational diabetes and their offspring.Journal of diabetes and metabolic disorders · 2025Review
- Machine Learning for Predicting the Transition From Gestational Diabetes to Type 2 Diabetes: A Systematic Review.Cureus · 2025Review
- Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes.Journal of diabetes science and technology · 2023Article
- TyGIS: improved triglyceride-glucose index for the assessment of insulin sensitivity during pregnancy.Cardiovascular diabetology · 2022Article
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) is a type of diabetes that usually resolves at the end of the pregnancy but exposes to a higher risk of developing type 2 diabetes mellitus (T2DM). This study aimed to unravel the factors, among those that quantify specific metabolic processes, which determine progression to T2DM by using machine-learning techniques. Classification of women who did progress to T2DM (labeled as PROG,
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.