Neonatal outcomes following maternal bariatric surgery: A systematic review and meta-analysis
Bibliographic record
Abstract
To evaluate neonatal outcomes in pregnancies following maternal bariatric surgery, focusing on preterm birth, small for gestational age (SGA), large for gestational age (LGA), congenital anomalies, perinatal mortality, and neonatal intensive care unit (NICU) admission, while exploring maternal health impacts and long-term neonatal effects.PubMed, Embase, Scopus, CINAHL, and Google Scholar were searched (inception to July 31, 2025) for observational studies and meta-analyses comparing neonatal outcomes in post-bariatric surgery pregnancies to controls (obese, BMI-matched, or general population).Random-effects meta-analyses calculated pooled odds ratios (ORs) with 95% confidence intervals (CIs).Subgroup analyses assessed surgery type (Roux-en-Y gastric bypass [RYGB], sleeve gastrectomy [SG], laparoscopic adjustable gastric banding [LAGB], biliopancreatic diversion [BPD]) and surgery-to-conception interval.Quality was evaluated using the Newcastle-Ottawa Scale and ROBINS-I tool.From 48 studies (20,500 post-bariatric surgery pregnancies, >4.5 million controls), bariatric surgery increased preterm birth (OR 1.58, 95% CI 1.39-1.80),SGA (OR 2.22, 95% CI 1.88-2.62),congenital anomalies (OR 1.31, 95% CI 1.05-1.63),perinatal mortality (OR 1.36, 95% CI 1.03-1.80),and NICU admission (OR 1.41, 95% CI 1.25-1.59),but reduced LGA (OR 0.39, 95% CI 0.31-0.49).SG showed lower risks for preterm birth (OR 1.33 vs. RYGB OR 1.78) and SGA (OR 1.33 vs. RYGB OR 2.52).Pregnancies <18 months post-surgery had higher SGA risks (OR 2.75).Maternal nutritional deficiencies (e.g., folate, B12) were linked to adverse outcomes.Maternal bariatric surgery increases neonatal risks, particularly with RYGB, driven by malabsorption.SG appears safer.Delayed conception (12-18 months), nutritional optimization, and multidisciplinary care are critical.Further research on SG-specific outcomes and long-term neonatal health is needed.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".