Evidence map›Paper›PMID 26110385›Full record

ReviewInternational journal of molecular sciences2015

Inflammatory and Other Biomarkers: Role in Pathophysiology and Prediction of Gestational Diabetes Mellitus.

Sally K Abell, Barbora De Courten, Jacqueline A Boyle, Helena J Teede

Open access · goldAbstract readReview
In one paragraph

Review in International journal of molecular sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
97citing papers in PubMed, 8 pooled it
13.6field-weighted citation impact, top 1% 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

97 citing papers in PubMed, 8 syntheses or guidelines pooled it, 240 citations in OpenAlex.

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37 more citing papers are in PubMed but not listed here.

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

4 authors at 1 institution in 1 country.

Sally K AbellMonash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton 3168, Australia. sally.abell@monash.edu.
Barbora De CourtenMonash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton 3168, Australia. barbora.decourten@monash.edu.
Jacqueline A BoyleMonash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton 3168, Australia. jacqueline.boyle@monash.edu.
Helena J TeedeMonash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton 3168, Australia. helena.teede@monash.edu.
Monash Health · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding pathophysiology and identifying mothers at risk of major pregnancy complications is vital to effective prevention and optimal management. However, in current antenatal care, understanding of pathophysiology of complications is limited. In gestational diabetes mellitus (GDM), risk prediction is mostly based on maternal history and clinical risk factors and may not optimally identify high risk pregnancies. Hence, universal screening is widely recommended. Here, we will explore the literature on GDM and biomarkers including inflammatory markers, adipokines, endothelial function and lipids to advance understanding of pathophysiology and explore risk prediction, with a goal to guide prevention and treatment of GDM.

Indexed as

BiomarkersDiabetes, GestationalFemaleHumansInflammation MediatorsPregnancyBiomarkersInflammation Mediatorsbiomarkersgestational diabetes mellitusinflammatory markerspregnancyrisk prediction

Identifiers

PMID26110385
PMCPMC4490503
OpenAlexW2164974383

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.