STAT trial: stoma or intestinal anastomosis for necrotizing enterocolitis: a multicentre randomized controlled trial
Bibliographic record
Abstract
PURPOSE: The STAT trial is a multicenter randomized controlled trial in 12 centers worldwide aiming to determine the most effective operation for neonates with necrotizing enterocolitis (NEC) requiring intestinal resection: stoma formation (ST) or primary anastomosis (PA). METHODS: Infants having a primary laparotomy for NEC were randomized intraoperatively to PA or ST if the operating surgeon thought that both were viable treatment options for that patient. The primary outcome (duration of parenteral nutrition [PN]) was evaluated by Cox regression. RESULTS: Eighty patients were recruited from 2010 to 2019. Infants undergoing anastomosis finished PN significantly earlier than patients undergoing stoma (hazard ratio PA vs. ST 2.38, 95% CI 1.36-4.12 p = 0.004). There was no difference in mortality between the two groups (PA 4/35 vs. ST 8/38 p = 0.35) or in the rate of complications requiring further unplanned operations (p = n.s.). Multiple intestinal complications were more frequent in the stoma group compared to the anastomosis group (ST 12/26 vs. PA 5/31, p = 0.02, Fisher's Exact test). CONCLUSION: At laparotomy for NEC, when there is no disease distal to resected intestine, primary anastomosis should be performed as it enhances the recovery from NEC, reduces the risk of multiple intestinal complications and does not increase adverse 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".