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Record W4389920907 · doi:10.1038/s43856-023-00393-8

Refining the diagnosis of gestational diabetes mellitus: a systematic review and meta-analysis

2023· review· en· W4389920907 on OpenAlexaff
Ellen C. Francis, Camille E. Powe, William L. Lowe, Sara L. White, Denise Scholtens, Jiaxi Yang, Yeyi Zhu, Cuilin Zhang, Marie‐France Hivert, Soo Heon Kwak, Arianne Sweeting

Bibliographic record

VenueCommunications Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsUniversité de MontréalPopulation Health Research InstituteUniversity of ManitobaMcMaster UniversityCentre Hospitalier Universitaire Sainte-JustineImpactUniversity of Calgary
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Heart, Lung, and Blood InstituteNational Health and Medical Research CouncilNovo Nordisk FondenNational Institute of Diabetes and Digestive and Kidney DiseasesRobert Wood Johnson FoundationLunds UniversitetNational Human Genome Research InstituteWellcome TrustAustralian Diabetes SocietyMedical Research CouncilEuropean Association for the Study of DiabetesNovo NordiskAmerican Diabetes AssociationNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsGestational diabetesMeta-analysisRefining (metallurgy)MedicineDiabetes mellitusObstetricsSystematic reviewMEDLINEPregnancyInternal medicineGestationEndocrinologyBiologyMetallurgyMaterials science

Abstract

fetched live from OpenAlex

Abstract Background Perinatal outcomes vary for women with gestational diabetes mellitus (GDM). The precise factors beyond glycemic status that may refine GDM diagnosis remain unclear. We conducted a systematic review and meta-analysis of potential precision markers for GDM. Methods Systematic literature searches were performed in PubMed and EMBASE from inception to March 2022 for studies comparing perinatal outcomes among women with GDM. We searched for precision markers in the following categories: maternal anthropometrics, clinical/sociocultural factors, non-glycemic biochemical markers, genetics/genomics or other -omics, and fetal biometry. We conducted post-hoc meta-analyses of a subset of studies with data on the association of maternal body mass index (BMI, kg/m 2 ) with offspring macrosomia or large-for-gestational age (LGA). Results A total of 5905 titles/abstracts were screened, 775 full-texts reviewed, and 137 studies synthesized. Maternal anthropometrics were the most frequent risk marker. Meta-analysis demonstrated that women with GDM and overweight/obesity vs. GDM with normal range BMI are at higher risk of offspring macrosomia (13 studies [ n = 28,763]; odds ratio [OR] 2.65; 95% Confidence Interval [CI] 1.91, 3.68), and LGA (10 studies [ n = 20,070]; OR 2.23; 95% CI 2.00, 2.49). Lipids and insulin resistance/secretion indices were the most studied non-glycemic biochemical markers, with increased triglycerides and insulin resistance generally associated with greater risk of offspring macrosomia or LGA. Studies evaluating other markers had inconsistent findings as to whether they could be used as precision markers. Conclusions Maternal overweight/obesity is associated with greater risk of offspring macrosomia or LGA in women with GDM. Pregnancy insulin resistance or hypertriglyceridemia may be useful in GDM risk stratification. Future studies examining non-glycemic biochemical, genetic, other -omic, or sociocultural precision markers among women with GDM are warranted.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.051
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0220.031
Bibliometrics0.0090.010
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.364
GPT teacher head0.471
Teacher spread0.107 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations29
Published2023
Admission routes1
Has abstractyes

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