Effects of Gestational Diabetes Mellitus on Fetal Liver Length: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".