SynthesisJournal of medical Internet research2022
Machine Learning Prediction Models for Gestational Diabetes Mellitus: Meta-analysis.
Synthesis in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 11 of them syntheses that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
57 citing papers in PubMed, 11 syntheses or guidelines pooled it, 115 citations in OpenAlex.
- Machine learning and deep learning for diagnosis of Polyendocrine Metabolic Ovarian Syndrome: systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Prediction Models for Maternal and Offspring Short- and Long-Term Outcomes Following Gestational Diabetes: A Systematic Review.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2025Pooled it
- Comparing the Performance of Machine Learning Models and Conventional Risk Scores for Predicting Major Adverse Cardiovascular Cerebrovascular Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- The Machine Learning Models in Major Cardiovascular Adverse Events Prediction Based on Coronary Computed Tomography Angiography: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Accuracy of machine learning in identifying candidates for total knee arthroplasty (TKA) surgery: a systematic review and meta-analysis.European journal of medical research · 2025Pooled 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
- Diagnostic accuracy of machine learning for endometriosis: a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Machine Learning and Deep Learning for Diagnosis of Lumbar Spinal Stenosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Machine learning prediction models for diabetic kidney disease: systematic review and meta-analysis.Endocrine · 2024Pooled it
- Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Safety and efficacy of brivaracetam in children epilepsy: a systematic review and meta-analysis.Frontiers in neurology · 2023Pooled it
- Are asynchronous or synchronous clinical decision support more likely to change provider behavior? A case study in dementia.Journal of the American Medical Informatics Association : JAMIA · 2026Trial
- Combined triple screening and BMI models for gestational diabetes prediction.Turkish journal of obstetrics and gynecology · 2026Article
- A three-metabolite microbiota-associated signature for early risk stratification of gestational diabetes mellitus.Cardiovascular diabetology · 2026Article
- Early prediction of gestational diabetes mellitus with clinical characteristics, cell-free DNA and genetic variants.Journal of translational medicine · 2026Article
- AI-powered population-based birth cohort study in the Western Province of Sri Lanka: study protocol.BMC pregnancy and childbirth · 2026Article
- Development and validation of a multidimensional indicator-based risk prediction model for gestational diabetes mellitus: a nested case-control study.BMC endocrine disorders · 2026Article
- Maternal serum NRF2 at 12 weeks as a biomarker for development of gestation diabetes mellitus.Archives of gynecology and obstetrics · 2026Article
- Artificial Intelligence in Obstetrics and Gynecology Nursing: Clinical, Educational, and Ethical Perspectives.Cureus · 2026Review
- Construction of an interpretable machine learning model for predicting gestational diabetes mellitus based on 45 dietary nutrients.BioData mining · 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
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGestational diabetes mellitus (GDM) is a common endocrine metabolic disease, involving a carbohydrate intolerance of variable severity during pregnancy. The incidence of GDM-related complications and adverse pregnancy outcomes has declined, in part, due to early screening. Machine learning (ML) models are increasingly used to identify risk factors and enable the early prediction of GDM.
objectiveThe aim of this study was to perform a meta-analysis and comparison of published prognostic models for predicting the risk of GDM and identify predictors applicable to the models.
methodsFour reliable electronic databases were searched for studies that developed ML prediction models for GDM in the general population instead of among high-risk groups only. The novel Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias of the ML models. The Meta-DiSc software program (version 1.4) was used to perform the meta-analysis and determination of heterogeneity. To limit the influence of heterogeneity, we also performed sensitivity analyses, a meta-regression, and subgroup analysis.
resultsA total of 25 studies that included women older than 18 years without a history of vital disease were analyzed. The pooled area under the receiver operating characteristic curve (AUROC) for ML models predicting GDM was 0.8492; the pooled sensitivity was 0.69 (95% CI 0.68-0.69; P<.001; I
conclusionsCompared to current screening strategies, ML methods are attractive for predicting GDM. To expand their use, the importance of quality assessments and unified diagnostic criteria should be further emphasized.
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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 Socratic graph.