Bowel ultrasound for predicting surgical management of necrotizing enterocolitis: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Necrotizing enterocolitis (NEC) is a devastating inflammatory disease of the intestinal tract that represents a significant source of morbidity and mortality in preterm infants. Imaging of the abdomen is valuable for timely diagnosis and close monitoring of disease progression in NEC. Bowel ultrasound (US) is increasingly being recognized as an important imaging tool for evaluating NEC that provides additional detail than plain abdominal radiographs. OBJECTIVE: To identify bowel US findings associated with surgical management or death in infants with NEC. MATERIALS AND METHODS: We searched Embase, PubMed, and the Cumulative Index to Nursing and Allied Health Literature for studies investigating the association between bowel US findings and surgical management or death in NEC. Selected articles were evaluated for quality of study methodology using the Newcastle-Ottawa Scale, and aggregate statistics for odds ratio (OR) and 95% confidence interval were calculated. RESULTS: Of 521 articles reviewed, 11 articles comprising 748 infants were evaluated for quality. Nine of the studies were retrospective and from single-center experiences. Pooled analysis showed that focal fluid collections (OR 17.9, 3.1-103.3), complex ascites (OR 11.3, 4.2-30.0), absent peristalsis (OR 10.7, 1.7-69.0), pneumoperitoneum (OR 9.6, 1.7-56.3), bowel wall echogenicity (OR 8.6, 3.4-21.5), bowel wall thinning (OR 7.11.6-32.3), absent perfusion (OR 7.0, 2.1-23.8), bowel wall thickening (OR 3.9, 2.4-6.1) and dilated bowel (OR 3.5, 1.8-6.8) were associated with surgery or death in NEC. In contrast, portal venous gas (OR 3.0, 0.8-10.6), pneumatosis intestinalis (OR 2.1, 0.9-5.1), increased bowel perfusion (OR 2.6, 0.6-11.1) and simple ascites (OR 0.54, 0.1-2.5) were not associated with surgery or death. CONCLUSION: This meta-analysis identified several bowel US findings that are associated and not associated with surgery or death in NEC. Bowel US may be useful for early identification of high-risk infants with NEC who may benefit from more aggressive treatment, including surgery. Future studies are needed to determine whether the addition of bowel US in NEC evaluation would improve 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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".