Evidence mapPaperPMID 40128088Full record

ReviewMedicine2025

Perspective on the nursing management for gestational diabetes mellitus: A perspective.

Ya-Ting Fan, Xin-Hui Wang, Qing Wang, Xiao-Tong Luo, Jing Cao

Abstract readReview
In one paragraph

Review in Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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

2 citing papers in PubMed.

  1. Article
  2. Review
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

5 authors.

Ya-Ting FanDepartment of Obstetrics, Baoji People's Hospital, Baoji, China.
Xin-Hui Wang
Qing Wang
Xiao-Tong Luo

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study provides a comprehensive examination of gestational diabetes mellitus (GDM), shedding light on the geographical and ethnic variations in its prevalence. It elucidates the diagnostic evolution, noting the transition from rudimentary glucose tests to the more sophisticated Oral Glucose Tolerance Test (OGTT), which not only facilitates early detection but also standardizes screening protocols. The study delves into the evolution of GDM diagnosis, emphasizing the standardization of the OGTT and its pivotal role in enhancing early detection rates. It meticulously discusses holistic management approaches for GDM, encompassing tailored dietary interventions, prescribed physical activity, and pharmacotherapy. The need for individualized strategies to optimize glucose control is strongly emphasized. The study underscores the significance of mental health in GDM management, advocating for integrated psychological support and stress management interventions to bolster metabolic regulation. An exploration of telemedicine and artificial intelligence highlights their potential to revolutionize GDM care by enabling real-time monitoring and personalized interventions, thus improving patient outcomes. An analysis of health policies and educational efforts underscores their impact on GDM management, advocating for proactive measures to mitigate its prevalence through public health initiatives. The study identifies key research gaps and offers a focused analysis of critical advancements in GDM management, including personalized care strategies and the role of innovative technologies such as artificial intelligence and telemedicine in improving outcomes. Finally, the study calls for further research into personalized treatment modalities and innovative diagnostic tools to address existing gaps in GDM management, particularly in diverse demographic groups.

Indexed as

Diabetes, GestationalFemaleGlucose Tolerance TestHumansPregnancyTelemedicine

Identifiers

PMID40128088
PMCPMC11936584

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.