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Record W4412687533 · doi:10.1111/anae.16705

Consensus recommendations for paediatric airway topicalisation using lidocaine

2025· review· en· W4412687533 on OpenAlexaff
H. A. Iliff, Julia Parnell, Paul Baker, Alistair Baxter, Rachel Chapman, James Coulson, Catherine Dotherty, Yasmin Endlich, Peter Frykholm, Jane Harkin, Narasimhan Jagannathan, Haytham Kubba, Jackson Kwizera Ndekezi, Barry McGuire, Ahmed Mesbah, Rania Mehanna, Alice Miskovic, Richard Newton, Nara Orban, Sarah A. Perry, James Peyton, Kate Rivett, Mari Roberts, Keno Temo

Bibliographic record

VenueAnaesthesia · 2025
Typereview
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineLidocaineConsensus conferenceDosingIntensive care medicineDelphi methodMEDLINEMultidisciplinary approachDelphiSystematic reviewAirwayAnesthesiaPharmacology

Abstract

fetched live from OpenAlex

INTRODUCTION: Lidocaine is commonly used to provide airway topicalisation in children. However, there is a paucity of evidence and no guidance available on safe dosing practices. METHODS: An international expert multidisciplinary, multi-society working group conducted a systematic review of the literature, followed by a three-round Delphi process to produce consensus recommendations. These recommendations aim to improve the safety of children having their airways topicalised with lidocaine. RESULTS: The systematic review identified 26 articles, with 21 recommendations agreed across five domains of practice: dosing; recovery; adverse reactions; institutional responsibilities; and learning from events. Evidence was limited and mainly grades C and D but the strength of the recommendations, based on the expert consensus, was mainly moderate (65-79% consensus) and strong (≥ 80% consensus). DISCUSSION: It is hoped these consensus recommendations will promote safe practice when lidocaine is used for airway topicalisation in children.

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)0.081
metaresearch head score (Gemma)0.196
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.081
Threshold uncertainty score0.428

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0810.196
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.013
Bibliometrics0.0160.009
Science and technology studies0.0020.003
Scholarly communication0.0060.007
Open science0.0100.007
Research integrity0.0100.011
Insufficient payload (model declined to judge)0.0110.005

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 head0.100
GPT teacher head0.400
Teacher spread0.300 · 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 designNot applicable
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

Citations1
Published2025
Admission routes1
Has abstractyes

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