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Record W4407868484 · doi:10.18502/ijph.v54i2.17903

Effects of Gestational Diabetes Mellitus on Fetal Liver Length: A Systematic Review and Meta-Analysis

2025· review· en· W4407868484 on OpenAlexaboutno aff
Sahar Ardalan Khales, Abdolhalim Rajabi, Masoud Golalipour, Gholamreza Roshandel, Mohammad Jafar Golalipour

Bibliographic record

VenueIranian Journal of Public Health · 2025
Typereview
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsnot available
Fundersnot available
KeywordsGestational diabetesMeta-analysisMedicineFetusDiabetes mellitusObstetricsGestational agePregnancyInternal medicineGestationBioinformaticsEndocrinologyBiology

Abstract

fetched live from OpenAlex

Background: Gestational diabetes mellitus (GDM) is a serious pregnancy complication that can affect various organs and organ systems of the mother and fetus. In diabetic mothers, increased blood glucose delivery to the fetus leads to fetal hyperglycemia and hyperinsulinemia, which promotes the growth of insulin-dependent organs such as the liver. Therefore, this systematic review and meta-analysis was conducted to more precisely estimate the association between GDM and fetal liver length (FLL). Methods: Six electronic databases (PubMed, Scopus, Web of Science, ProQuest, Cochrane, and Wiley) were searched up to Aug 2023. Two reviewers independently extracted data and assessed the risk of bias using the Newcastle-Ottawa Scale. The pooled weighted and standardized mean differences in FLL were calculated using random-effects models. Heterogeneity, subgroup analysis, and publication bias were also assessed using funnel plots. All statistical analyses were performed using Stata Version 16.0. Results: <0.001) trimesters of pregnancy. The pooled mean difference in FLL between the GDM and non-GDM groups was 4.85 mm (WMD=4.85; 95% CI: 3.26, 6.45), indicating larger liver size in fetuses from mothers with GDM. Conclusion: GDM is a significant risk factor for increased FLL, as assessed by ultrasound, which may reflect fetal overgrowth and metabolic dysfunction.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.660
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0090.003
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.114
GPT teacher head0.394
Teacher spread0.280 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations0
Published2025
Admission routes1
Has abstractyes

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