Evidence map›Paper›PMID 40320381›Full record

SynthesisObesity reviews : an official journal of the International Association for the Study of Obesity2025

Prediction Models for Maternal and Offspring Short- and Long-Term Outcomes Following Gestational Diabetes: A Systematic Review.

Yasmina Al Ghadban, Nerys M Astbury, Abdallah Kurdi, Ankita Sharma, Beatrice Ope, Tzu-Ying Liu, Lucy MacKillop, Huiqi Y Lu, Jane E Hirst

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Obesity reviews : an official journal of the International Association for the Study of Obesity, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. 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 · 2025
    Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yasmina Al GhadbanNuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK.ORCID 0000-0002-7533-9231
Nerys M AstburyNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.ORCID 0000-0001-9301-7458
Abdallah KurdiDepartment of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon.ORCID 0000-0002-8321-4025
Ankita SharmaNuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK.
Beatrice OpeFaculty of Medicine, School of Public Health, Imperial College London, London, UK.
Tzu-Ying LiuNuffield Department of Population Health, University of Oxford, Oxford, UK.
Lucy MacKillopNuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK.
Huiqi Y LuInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
Jane E HirstNuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK.

Funding

Diabetes Research and Wellness Foundation and NIHR Biomedical Research Centre OxfordMRC iCase Doctoral Training ProgramNIHR School of Primary Care Research, Diabetes UK 0006324NIHR School of Primary Care Research, Diabetes UK 21/0006324Royal Academy of Engineering Daphne Jackson Research Fellowship EP/N020774/1UKRI Future Leaders Fellowship
6 · The paper itself

Abstract

objectivesGestational diabetes mellitus (GDM), affecting one in seven pregnant women worldwide, can have short- and long-term adverse outcomes for both the mother and her baby. Despite a raft of prognostic models aiming to predict adverse GDM outcomes, very few have impacted clinical practice. This systematic review summarizes and critically evaluates prediction models for GDM outcomes, to identify promising models for further evaluation.

methodsWe searched EMBASE, MEDLINE, Web of Science, CINAHL, and CENTRAL for studies that reported the development or validation of predictive models for GDM outcomes in mother or offspring (PROSPERO: CRD42023396697).

resultsSixty-four articles detailing 103 developed and 12 validated models were included in this review. Of these, 45% predicted long term, 31% birth, and 23% pregnancy outcomes. Most models (87%) had a high risk of bias, lacking sufficient outcome events, internal validation, or proper calibration. Only eight models were found at low risk of bias.

conclusionsOur findings highlight a gap in rigorously developed prediction models for adverse GDM outcomes. There is a need to further validate existing models and evaluate their clinical utility to generate risk prediction tools capable of improving clinical decision-making for women with GDM and their children.

Indexed as

Diabetes, GestationalPregnancy OutcomeFemaleHumansInfant, NewbornPregnancyPrognosisRisk Assessmentgestational diabetesprediction modelsprognosis modeling

Identifiers

PMID40320381
PMCPMC12318903

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.