Evidence map›Paper›PMID 31324151›Full record

ArticleBMC pregnancy and childbirth2019

A simple model to predict risk of gestational diabetes mellitus from 8 to 20 weeks of gestation in Chinese women.

Tao Zheng, Weiping Ye, Xipeng Wang, Xiaoyong Li, Jun Zhang, Julian Little, Lixia Zhou, Lin Zhang

Open access · goldAbstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 2 pooled it
8.8field-weighted citation impact, top 2% of its field
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

41 citing papers in PubMed, 2 syntheses or guidelines pooled it, 83 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Article
  6. Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2025
    Review
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  8. Review
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  14. A Simplified Screening Model to Predict the Risk of Gestational Diabetes Mellitus in Pregnant Chinese Women.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2023
    Article
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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

8 authors at 2 institutions in 2 countries.

Tao ZhengObstetric and Gynecology Department, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Weiping YeObstetric and Gynecology Department, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xipeng WangObstetric and Gynecology Department, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiaoyong LiEndocrinology Department, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jun ZhangObstetric and Gynecology Department, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Julian LittleSchool of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada.
Lixia ZhouObstetric and Gynecology Department, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lin ZhangObstetric and Gynecology Department, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. zhanglin01@xinhuamed.com.cn.ORCID http://orcid.org/0000-0002-2728-4715
Shanghai Jiao Tong University · CNUniversity of Ottawa · CA

Funding

School of Medicine, Shanghai Jiao Tong University (CN) 15QT12Science and Technology Commission of Shanghai Municipality 15411953200Shanghai Hospital Development Center SHDC12016204
6 · The paper itself

Abstract

backgroundGestational diabetes mellitus (GDM) is associated with adverse perinatal outcomes. Screening for GDM and applying adequate interventions may reduce the risk of adverse outcomes. However, the diagnosis of GDM depends largely on tests performed in late second trimester. The aim of the present study was to bulid a simple model to predict GDM in early pregnancy in Chinese women using biochemical markers and machine learning algorithm.

methodsData on a total of 4771 pregnant women in early gestation were used to fit the GDM risk-prediction model. Predictive maternal factors were selected through Bayesian adaptive sampling. Selected maternal factors were incorporated into a multivariate Bayesian logistic regression using Markov Chain Monte Carlo simulation. The area under receiver operating characteristic curve (AUC) was used to assess discrimination.

resultsThe prevalence of GDM was 12.8%. From 8th to 20th week of gestation fasting plasma glucose (FPG) levels decreased slightly and triglyceride (TG) levels increased slightly. These levels were correlated with those of other lipid metabolites. The risk of GDM could be predicted with maternal age, prepregnancy body mass index (BMI), FPG and TG with a predictive accuracy of 0.64 and an AUC of 0.766 (95% CI 0.731, 0.801).

conclusionsThis GDM prediction model is simple and potentially applicable in Chinese women. Further validation is necessary.

Indexed as

Diabetes, GestationalAdultBlood GlucoseBody Mass IndexChinaFemaleGlucose Tolerance TestHumansMass ScreeningMaternal AgePredictive Value of TestsPregnancyPregnancy Trimester, FirstPrognosisRisk AssessmentRisk FactorsBlood GlucoseGestational diabetes mellitusMaternal factorsRisk prediction

Identifiers

PMID31324151
PMCPMC6642502
OpenAlexW2963026316

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.