Enhanced Recovery After Surgery (ERAS) consensus recommendations for opioid-minimising pharmacological neonatal pain management
Bibliographic record
Abstract
OBJECTIVE: Enhanced recovery after surgery (ERAS) guidelines have been successfully applied to children and neonates. There is a need to provide evidence-based consensus recommendations to manage neonatal pain perioperatively to ensure adequate analgesia while minimising harmful side effects. METHODS: Following a stakeholder needs assessment, an international guideline development committee (GDC) was established. A modified Delphi consensus iteratively defined the scope of patient and procedure inclusion, topic selection and recommendation content regarding the pharmacologic management of neonatal pain. Critical appraisal tools assessed the relevance and quality of full-text studies. Each recommendation underwent a formal Grades of Recommendation, Assessment, Development and Evaluation (GRADE) assessment of the quality of evidence and expert consensus was used to determine the strength of recommendations. RESULTS: The GDC included paediatric anaesthesiologists, surgeons, and ERAS methodology experts. The population was defined as neonates at >32 weeks gestational age within 30 days of life undergoing surgery or painful procedures associated with surgery. Topic selection targeted pharmacologic opioid-minimising strategies. A total of 4249 abstracts were screened for non-opioid analgesia and 738 abstracts for the use of locoregional analgesia. Full-text review of 18 and 9 articles, respectively, resulted in two final recommendations with a moderate quality of evidence to use regular acetaminophen and to consider the use of locoregional analgesia. There was inadequate evidence to guide the use of other non-opioid adjuncts in this population. CONCLUSIONS: Evidence-based, ERAS-driven consensus recommendations were developed to minimise opioid usage in neonates. Further research is required in this population to optimize multimodal strategies for pain control.
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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 | 0.085 | 0.156 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".