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Record W4307056301 · doi:10.1093/pch/pxac100.059

60 Inhaled nitrous oxide for distressing procedures in children: a systematic review

2022· review· en· W4307056301 on OpenAlexaff
Naveen Poonai, Christopher Creene, Aldo Ariel Dobrowlanski, Rishika Geda, Lisa Hartling, Samina Ali, Maala Bhatt, Evelyne D Trottier, Vikram Sabhaney, Katie O’Hearn, Martin H. Osmond, Rini Jain

Bibliographic record

VenuePaediatrics & Child Health · 2022
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsChildren's Hospital of Eastern OntarioBC Children's HospitalUniversity of AlbertaCentre Hospitalier Universitaire Sainte-JustineWestern University
Fundersnot available
KeywordsMedicineAdverse effectCINAHLRandomized controlled trialCochrane LibraryDistressMEDLINEMeta-analysisAnesthesiaPhysical therapyInternal medicinePsychiatryClinical psychology

Abstract

fetched live from OpenAlex

Abstract Background Inhaled nitrous oxide (N2O) is a potentially effective agent for pain and procedural distress in children but questions remain regarding indication specific effectiveness. Objectives Our objective was to synthesize the evidence for N2O in children and youth regarding procedural distress, pain, and adverse events (AEs). Design/Methods We performed electronic searches of MEDLINE, EMBASE, Google Scholar, CINAHL, conference proceedings, and trial registries. We included randomized trials of N2O in children and youth 0-21.99 years that reported procedural distress or pain. Methodological rigor and quality of evidence were evaluated using the Cochrane Collaboration’s Risk of Bias tool and the Grading of Recommendations Assessment, Development, and Evaluation system, respectively. Where meta-analysis wasn’t possible, we summarized results using Tricco et al.’s classification system of “favorable” or “unfavorable” (p<0.05), or “neutral” (p>0.05). Results We included 29 trials, involving 2,404 children aged 3 weeks-21 years. The overall quality of evidence for distress and pain was “low” and “moderate”, respectively. For venous cannulation (n=12), three meta-analyses were possible: A) pain was significantly lower with 70% N2O versus eutectic mixture of local anesthetics (EMLA) (mean difference: -16.5; 95% CI: -28.6 to -4.4; p=0.008; 85 participants; 3 trials; I2= 0%); B) pain was not significantly different with 50% N2O alone versus EMLA (mean difference: -0.4; 95% CI: -1.2 to 0.3; p=0.26; 65 participants; 2 trials; I2= 15%); C) combination 50% N2O plus EMLA was significantly better than EMLA alone (mean difference: -1.2; 95% CI: -2.1 to -0.3; p=0.007; 65 participants; 2 trials; I2= 43%). For pain and distress during laceration repair (n=5), N2O was deemed “favorable” versus subcutaneous lidocaine, oxygen, or oral midazolam, but “neutral” versus intravenous ketamine. For pain and distress during fracture reduction (n=3), N2O was deemed “neutral” versus combination intramuscular meperidine plus promethazine, intravenous lidocaine, or combination intravenous ketamine plus midazolam. For pain and distress during lumbar puncture (n=1), N2O was deemed “favorable” versus oxygen. Higher concentrations of N20 were associated with more AEs per participant: 6.7% (1/15), 13.7% (64/468), and 25.3% (56/221) with 30%, 50%, and 70% N2O, respectively. The most common AEs were nausea and agitation (both 3.5% [40/1128]). There were no AEs requiring resuscitative measures. Conclusion N2O is a potentially effective agent for reducing procedural distress and pain in children, although high quality evidence is lacking. Most data exist for venous cannulation where safety and efficacy at reducing pain are optimized with combining 50% N2O and topical anesthetic cream. For laceration repair, there is considerably less data. Still, N2O appears to be superior to oral midazolam but equivalent to intravenous ketamine.

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)0.009
metaresearch head score (Gemma)0.031
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 score0.010
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0100.011
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.350
Teacher spread0.319 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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