Laparotomy versus Peritoneal Drainage as Primary Treatment for Surgical Necrotizing Enterocolitis or Spontaneous Intestinal Perforation in Preterm Neonates: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
AIM: to systematically review and meta-analyze the impact on morbidity and mortality of peritoneal drainage (PD) compared to laparotomy (LAP) in preterm neonates with surgical NEC (sNEC) or spontaneous intestinal perforation (SIP). METHODS: Medical databases were searched until June 2022 for studies comparing PD and LAP as primary surgical treatment of preterm neonates with sNEC or SIP. The primary outcome was survival during hospitalization; predefined secondary outcomes included need for parenteral nutrition at 90 days, time to reach full enteral feeds, need for subsequent laparotomy, duration of hospitalization and complications. RESULTS: Three RCTs (N = 493) and 49 observational studies (N = 19,447) were included. No differences were found in the primary outcome for RCTs, but pooled observational data showed that, compared to LAP, infants with sNEC/SIP who underwent PD had lower survival [48 studies; N = 19,416; RR 0.85; 95% CI 0.79-0.90; GRADE: low]. Observational studies also showed that the subgroup of infants with sNEC had increased survival in the LAP group (30 studies; N = 9370; RR = 0.82; 95% CI 0.72-0.91; GRADE: low). CONCLUSIONS: Compared to LAP, PD as primary surgical treatment for sNEC or SIP has similar survival rates when analyzing data from RCTs. PD was associated with lower survival rates in observational studies.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.005 | 0.005 |
| 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) | 0.003 | 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".