Diagnosis and Management of Necrotizing Enterocolitis: An International Survey of Neonatologists and Pediatric Surgeons
Bibliographic record
Abstract
BACKGROUND: Necrotizing enterocolitis (NEC) is a serious complication of prematurity. Currently, there is limited evidence to guide investigation and treatment strategies. OBJECTIVES: To evaluate the parameters used to diagnose or exclude NEC, and to identify differences between neonatologists and pediatric surgeons. METHODS: A scenario-based survey was sent to neonatologists and pediatric surgeons. RESULTS: 173 physicians from 26 countries completed the survey (55% neonatologists and 45% pediatric surgeons). Bloody stools, abdominal tenderness, low platelet counts, and increased lactate levels increased the likelihood of NEC for 82, 72, 56, and 45% of respondents, respectively. Intestinal pneumatosis, portal venous gas, and pneumoperitoneum on X-ray increased the likelihood of NEC for 99, 98, and 92% of respondents, respectively. Clinical examination and laboratory tests were insufficient to exclude NEC, but normal intestinal movements and normal gut wall thickness on ultrasonography decreased the likelihood of NEC for 38 and 33% of respondents, respectively. Neonatologists more frequently relied on increased gastric residuals and abdominal distension to diagnose NEC (p = 0.04 and p = 0.03, respectively), whereas pediatric surgeons more frequently reported that absence of bloody stools helped to exclude NEC (p = 0.04). In a deteriorating patient with suspected NEC, 39% of respondents would broaden the antibiotic spectrum, and 42% would recommend a laparotomy. CONCLUSION: Our results indicate a wide variation in the management of NEC, with significant differences between neonatologists and pediatric surgeons. A better appreciation of the relative significance and weighting that should be applied to the clinical features and investigations should reduce the variation in interpretation that appears to exist.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".