SynthesisInternational journal of environmental research and public health2020
The Unrealised Potential for Predicting Pregnancy Complications in Women with Gestational Diabetes: A Systematic Review and Critical Appraisal.
Synthesis in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- 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
- Diagnostic Gap in Rural Maternal Health: Initial Validation of a Parsimonious Clinical Model for Hypertensive Disorders of Pregnancy in a Honduran Hospital.Diagnostics (Basel, Switzerland) · 2026Article
- Diabetes polygenic risk scores and maternal characteristics in Hoosier Moms Cohort participants.Pregnancy (Hoboken, N.J.) · 2025Article
- Evaluating the Adipose Tissue Depth as a Predictor Factor for Gestational Diabetes in Later Pregnancy-A Systematic Review.Biomedicines · 2023Review
- Development and validation of risk prediction models for large for gestational age infants using logistic regression and two machine learning algorithms.Journal of diabetes · 2023Article
- Development, validation and clinical utility of a risk prediction model for adverse pregnancy outcomes in women with gestational diabetes: The PeRSonal GDM model.EClinicalMedicine · 2022Article
- Does poor methodological quality of prediction modeling studies translate to poor model performance? An illustration in traumatic brain injury.Diagnostic and prognostic research · 2022Article
- Are newly introduced criteria for the diagnosis of gestational diabetes mellitus associated with improved pregnancy outcomes and/or increased interventions in New South Wales, Australia? A population-based data linkage study.BMJ open diabetes research & care · 2021Article
- Maternal medicine in the COVID era.Best practice & research. Clinical obstetrics & gynaecology · 2021Review
- Association of pre- and early-pregnancy factors with the risk for gestational diabetes mellitus in a large Chinese population.Scientific reports · 2021Article
- Article
- ENDOCRINOLOGY IN THE TIME OF COVID-19: Diagnosis and management of gestational diabetes mellitus.European journal of endocrinology · 2020Article
- Application of Predictive Analytics in Pregnancy, Birth, and Postpartum Nursing Care.MCN. The American journal of maternal child nursingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gestational diabetes (GDM) increases the risk of pregnancy complications. However, these risks are not the same for all affected women and may be mediated by inter-related factors including ethnicity, body mass index and gestational weight gain. This study was conducted to identify, compare, and critically appraise prognostic prediction models for pregnancy complications in women with gestational diabetes (GDM). A systematic review of prognostic prediction models for pregnancy complications in women with GDM was conducted. Critical appraisal was conducted using the prediction model risk of bias assessment tool (PROBAST). Five prediction modelling studies were identified, from which ten prognostic models primarily intended to predict pregnancy complications related to GDM were developed. While the composition of the pregnancy complications predicted varied, the delivery of a large-for-gestational age neonate was the subject of prediction in four studies, either alone or as a component of a composite outcome. Glycaemic measures and body mass index were selected as predictors in four studies. Model evaluation was limited to internal validation in four studies and not reported in the fifth. Performance was inadequately reported with no useful measures of calibration nor formal evaluation of clinical usefulness. Critical appraisal using PROBAST revealed that all studies were subject to a high risk of bias overall driven by methodologic limitations in statistical analysis. This review demonstrates the potential for prediction models to provide an individualised absolute risk of pregnancy complications for women affected by GDM. However, at present, a lack of external validation and high risk of bias limit clinical application. Future model development and validation should utilise the latest methodological advances in prediction modelling to achieve the evolution required to create a useful clinical tool. Such a tool may enhance clinical decision-making and support a risk-stratified approach to the management of GDM. Systematic review registration: PROSPERO CRD42019115223.
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.