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Record W4406240888 · doi:10.1016/j.jnn.2024.12.006

A systematic review of clinical practice guidelines for the assessment and management of pain in neonates

2025· review· en· W4406240888 on OpenAlexfundno aff
Natasha Campbell, Pauline Adair, Nicola Doherty, D. McCormack, Amy Walsh

Bibliographic record

VenueJournal of Neonatal Nursing · 2025
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsClinical PracticeMedicinePain assessmentIntensive care medicinePain managementBusinessNursingPhysical therapy

Abstract

fetched live from OpenAlex

This systematic review provides a content analysis of the specific recommendations provided within existing clinical practice guidelines for the assessment and management of procedural pain in neonates. A structured search was completed in November 2021 in MEDLINE, Embase, and CINAHL to identify relevant clinical practice guidelines. Clinical practice guidelines were quality assessed using the Appraisal of Guidelines for Research and Evaluation II Instrument. 20 clinical practice guidelines were included. Recommendations and evidence provided within the guidelines for the assessment and management of procedural pain in neonates was variable. Improvements are required in relation to the vagueness of recommendations and inconsistencies. Translating evidence into practice is a complicated process that requires behavioural change amongst healthcare professionals. The Evidence-Based Practice for Improving Quality an adaptable multifaceted intervention can be implemented to change behaviour and reduce the evidence to practice gap.

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)0.023
metaresearch head score (Gemma)0.112
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: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.112
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0220.020
Science and technology studies0.0020.001
Scholarly communication0.0030.004
Open science0.0030.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.129
GPT teacher head0.546
Teacher spread0.418 · 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

Citations7
Published2025
Admission routes1
Has abstractyes

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Same venueJournal of Neonatal NursingSame topicPediatric Pain Management TechniquesFrench-language works237,207