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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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.898
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.100
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 teacher head, 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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