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Record W4410213779 · doi:10.1136/bmjpo-2024-003280

Enhanced Recovery After Surgery (ERAS) consensus recommendations for non-pharmacological perioperative neonatal pain management

2025· review· en· W4410213779 on OpenAlexaff
Brandon Pentz, Kristin Short, Mercedes Pilkington, Tyara Marchand, Saffa Aziz, Jennifer Lam, Adam O. Spencer, Megan A. Brockel, Scott Else, Duncan McLuckie, Andrew D. Franklin, David de Beer, Mehul V. Raval, Michael J. Scott, Mary Brindle

Bibliographic record

VenueBMJ Paediatrics Open · 2025
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsAlberta Children's HospitalWestern UniversityVictoria General HospitalHospital for Sick ChildrenUniversity of British ColumbiaUniversity of Calgary
Fundersnot available
KeywordsMedicinePain managementPerioperativeOpioidIntensive care medicineAnesthesia

Abstract

fetched live from OpenAlex

Enhanced Recovery After Surgery (ERAS) recommendations for multi-modal, opioid-limiting analgesia have been shown to be effective in paediatric patients. However, neonatal-specific protocols are limited, and protocols that address non-pharmacological approaches are rare. Through systematic review and modified Delphi consensus with anaesthesiologists, paediatric surgeons and researchers, we developed non-pharmacological recommendations to improve perioperative neonatal pain management. Evidence from 37 articles of variable quality informed these recommendations. Our consensus resulted in four recommendations: (1) use of sweet-tasting solutions, (2) non-nutritive sucking, (3) skin-to-skin and (4) music therapy. These recommendations aim to reduce neonatal opioid usage alongside pharmacological therapies. PROSPERO registration number: CRD42021265273.

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 imitation

Not 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.

metaresearch head score (Codex)0.076
metaresearch head score (Gemma)0.158
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.076
Threshold uncertainty score0.404

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0760.158
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.012
Bibliometrics0.0100.006
Science and technology studies0.0020.001
Scholarly communication0.0030.004
Open science0.0060.006
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0120.004

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.

Opus teacher head0.069
GPT teacher head0.418
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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