Maternal and neonatal outcomes of singleton versus twin pregnancies complicated by gestational diabetes mellitus: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: There is limited evidence exploring the maternal and neonatal complications of gestational diabetes mellitus (GDM) following singleton or twin pregnancies. Further, there have been no reviews completed examining the possible risk factors associated with GDM in singleton compared to twin pregnancies. This study assesses the impact of GDM in singleton and twin pregnancies on maternal and neonatal outcomes. METHODS: From 1954 to December 2021, a thorough literature search was conducted in the EMBASE, Cochrane, MEDLINE, ScienceDirect, and Google Scholar databases and search engines. The risk of bias was calculated using the Newcastle Ottawa (NO) scale. A random-effects model was applied and interpreted as pooled odds ratio (OR) with 95% confidence intervals (CI). RESULTS: Eight studies satisfied the inclusion criteria, with the quality of most studies being good to satisfactory. The risk of caesarean section (pooled OR = 0.32; 95%CI: 0.22 to 0.46), small-for-gestational age (SGA) neonates (pooled OR = 0.40; 95%CI: 0.19 to 0.84), preterm delivery (pooled OR = 0.07; 95%CI: 0.06 to 0.09), respiratory morbidity (pooled OR = 0.26; 95%CI: 0.19 to 0.37), neonatal hyperbilirubinemia (pooled OR = 0.19; 95%CI: 0.10 to 0.40), and NICU admission (pooled OR = 0.18; 95%CI: 0.14 to 0.25) was significantly lower in singleton pregnancies with GDM than in twin pregnancies with GDM. CONCLUSION: Maternal outcomes like caesarean section and neonatal outcomes like SGA neonates, preterm delivery, respiratory morbidity, hyperbilirubinemia, and NICU admission were significantly greater in twin pregnancies with GDM. It is important for clinicians and policymakers to focus intervention strategies on twin pregnancies with GDM.
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 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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".