Fetal Meconium Peritonitis – Prenatal Findings and Postnatal Outcome: A Case Series, Systematic Review, and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: To describe the postnatal outcome of fetal meconium peritonitis and identify prenatal predictors of neonatal surgery. METHODS: We retrospectively reviewed all fetuses with ultrasound findings suspicious for meconium peritonitis at a single center over a 10-year period. A systematic review and meta-analysis were then performed pooling our results with previous studies assessing prenatally diagnosed meconium peritonitis and postnatal outcome. Prenatal sonographic findings were analyzed to identify predictors for postnatal surgery. RESULTS: 34 cases suggestive of meconium peritonitis were diagnosed at our center. These were pooled with cases from 14 other studies yielding a total of 244 cases. Postnatal abdominal surgery was required in two thirds of case (66.5 %). The strongest predictor of neonatal surgery was meconium pseudocyst (OR [95 % CI] 6.75 [2.53-18.01]), followed by bowel dilation (OR [95 % CI] 4.17 [1.93-9.05]) and ascites (OR [95 % CI] 2.57 [1.07-5.24]). The most common cause of intestinal perforation and meconium peritonitis, found in 52.2 % of the cases, was small bowel atresia. Cystic fibrosis was diagnosed in 9.8 % of cases. Short-term neonatal outcomes were favorable, with a post-operative mortality rate of 8.1 % and a survival rate of 100 % in neonates not requiring surgery. CONCLUSION: Meconium pseudocysts, bowel dilation, and ascites are prenatal predictors of neonatal surgery in cases of meconium peritonitis. Fetuses with these findings should be delivered in centers with pediatric surgery services. Though the prognosis is favorable, cystic fibrosis complicates postnatal outcomes.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.017 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".