Consensus Guidelines for Perioperative Care in Neonatal Intestinal Surgery: Enhanced Recovery After Surgery (ERAS <sup>®</sup> ) Society Recommendations
Bibliographic record
Abstract
Abstract Background Enhanced Recovery After Surgery (ERAS ® ) Society guidelines integrate evidence‐based practices into multimodal care pathways that have improved outcomes in multiple adult surgical specialties. There are currently no pediatric ERAS ® Society guidelines. We created an ERAS ® guideline designed to enhance quality of care in neonatal intestinal resection surgery. Methods A multidisciplinary guideline generation group defined the scope, population, and guideline topics. Systematic reviews were supplemented by targeted searching and expert identification to identify 3514 publications that were screened to develop and support recommendations. Final recommendations were determined through consensus and were assessed for evidence quality and recommendation strength. Parental input was attained throughout the process. Results Final recommendations ranged from communication strategies to antibiotic use. Topics with poor‐quality and conflicting evidence were eliminated. Several recommendations were combined. The quality of supporting evidence was variable. Seventeen final recommendations are included in the proposed guideline. Discussion We have developed a comprehensive, evidence‐based ERAS guideline for neonates undergoing intestinal resection surgery. This guideline, and its creation process, provides a foundation for future ERAS guideline development and can ultimately lead to improved perioperative care across a variety of pediatric surgical specialties.
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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 | 0.034 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".