Evidence map›Paper›PMID 27087160›Full record

SynthesisJournal of clinical research in pediatric endocrinology2016

The Relationship between Maternal Gestational Impaired Glucose Tolerance and Risk of Large-for-Gestational-Age Infant: A Meta-Analysis of 14 Studies.

Hai Qing Wang, Han Lin Lai, Yi Li, Qi Fei Liu, Shuang Hu, Li Li

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in Journal of clinical research in pediatric endocrinology, 2016. 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
1.3field-weighted citation impact, top 18% 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Effect of Intrahepatic Cholestasis of Pregnancy on Neonatal Birth Weight: A Meta-Analysis.Journal of clinical research in pediatric endocrinology · 2018
    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

6 authors at 1 institution in 1 country.

Hai Qing Wang
Han Lin Lai
Yi Li
Qi Fei Liu
Shuang Hu
Li LiAnhui Medical University School of Public Health, Department of Nutrition and Food Hygiene, Anhui, China, E-mail: li1964li@163.com.
Anhui Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo explore, by conducting a meta-analysis, whether gestational impaired glucose tolerance (IGT) is an independent predictor of neonatal large for gestational age (LGA) or not.

methodsMedline, Embase, and Cochrane Library databases were searched to identify published epidemiological studies (cohort and case-control studies) investigating the association between gestational IGT and neonatal LGA. Calculations of pooled estimates were conducted in random-effect models or fixed-effects models. Heterogeneity was tested by using chi-square test and I2 statistics. Egger's test (linear regression method) and Begg's test (rank correlation method) were used to assess potential publication bias.

resultsFourteen observational studies were included in the meta-analysis. The overall risk for the effect of IGT on LGA was 2.09 (1.56, 2.78). Stratified analyses showed no differences regarding different geographic regions or the analysis of overall adjusted odds ratios. No evidence of publication bias was observed in either Egger's test or Begg's test results.

conclusionGestational IGT is an independent predictor of neonatal LGA.

Indexed as

Birth WeightCase-Control StudiesCohort StudiesDiabetes, GestationalFemaleGestational AgeGlucose IntoleranceHumansInfant, NewbornOdds RatioOverweightPregnancyRisk AssessmentRisk Factors

Identifiers

PMID27087160
PMCPMC5096488
OpenAlexW2417939770

What OpenQuestion 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 OpenQuestion graph.