Enhanced Recovery After Surgery (ERAS) Society Recommendations for Neonatal Perioperative Care
Bibliographic record
Abstract
Importance: Neonates requiring surgery are often cared for in neonatal intensive care units (NICUs). Despite a breadth of surgical pathology, neonates share many perioperative priorities that allow for the development of unit-wide evidence-based Enhanced Recovery After Surgery (ERAS) recommendations. Observations: The guideline development committee included pediatric surgeons, anesthesiologists, neonatal nurses, and neonatologists in addition to ERAS content and methodology experts. The patient population was defined as neonates (first 28 days of life) undergoing a major noncardiac surgical intervention while admitted to a NICU. After the first round of a modified Delphi technique, 42 topics for potential inclusion were developed. There was consensus to develop a search strategy and working group for 21 topic areas. A total of 5763 abstracts were screened, of which 98 full-text articles, ranging from low to high quality, were included. A total of 16 recommendations in 11 topic areas were developed with a separate working group commissioned for analgesia-related recommendations. Topics included team communication, preoperative fasting, temperature regulation, antibiotic prophylaxis, surgical site skin preparation, perioperative ventilation, fluid management, perioperative glucose control, transfusion thresholds, enteral feeds, and parental care encouragement. Although clinically relevant, there were insufficient data to develop recommendations concerning the use of nasogastric tubes, Foley catheters, and central lines. Conclusions and Relevance: Despite varied pathology, neonatal perioperative care within NICUs allows for unit-based ERAS recommendations independent of the planned surgical procedure. The 16 recommendations within this ERAS guideline are intended to be implemented within NICUs to benefit all surgical neonates.
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 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 | 0.043 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".