The Effect of Mild Gestational Diabetes Mellitus Treatment on Adverse Pregnancy Outcomes: A Systemic Review and Meta-Analysis
Bibliographic record
Abstract
Objectives: It is uncertain whether the treatment of mild gestational diabetes mellitus (GDM) improves pregnancy outcomes. The aim of this systemic review and meta-analysis was to investigate the effect of mild GDM treatment on adverse pregnancy outcomes. Methods: A comprehensive literature search was conducted on the databases of PubMed, Scopus, and Google Scholar to retrieve studies that compared interventions for the treatment of mild GDM with usual antenatal care. The fixed/random effects models were used for the analysis of heterogeneous and non-heterogeneous results. Publication bias was assessed using the Harbord test. Also, the DerSimonian and Laird, and inverse variance methods were used to calculate the pooled odds ratio of events. The quality assessment of the included studies was performed using the Modified Newcastle-Ottawa Quality Assessment scale and the CONSORT checklist. In addition, the risk of bias was evaluated using the Cochrane Collaboration's tool for assessing risk of bias. Results: The systematic review and meta-analysis involved ten studies consisting of 3317 pregnant women who received treatment for mild GDM and 4407 untreated counterparts. Accordingly, the treatment of mild GDM significantly reduced the risk of macrosomia (OR = 0.3; 95%CI = 0.3-0.4), large for gestational age (OR = 0.4; 95%CI = 0.3-0.5), shoulder dystocia (OR = 0.3; 95%CI = 0.2-0.6), caesarean-section (OR = 0.8; 95%CI = 0.7-0.9), preeclampsia (OR = 0.4; 95%CI = 0.3-0.6), elevated cord C-peptide (OR = 0.7; 95%CI = 0.6-0.9), and respiratory distress syndrome (OR = 0.7; 95%CI = 0.5-0.9) compared to untreated counterparts. Moreover, the risk of induced labor significantly increased in the treated group compared to the untreated group (OR = 1.3; 95%CI = 1.0-1.6). However, no statistically significant difference was observed between the groups in terms of small for gestational age, hypoglycemia, hyperbilirubinemia, birth trauma, admission to the neonatal intensive care unit, and preterm birth. Sensitivity analysis based on the exclusion of secondary analysis data was all highly consistent with the main data analysis. Conclusion: Treatment of mild GDM reduced the risk of selected important maternal outcomes including preeclampsia, macrosomia, large for gestational age, cesarean section, and shoulder dystocia without increasing the risk of small for gestational age. Nevertheless, the treatment could not reduce the risk of neonatal metabolic abnormalities or several complications in newborn.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".