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Record W3168446116 · doi:10.1007/s12630-021-02007-0

Canadian Airway Focus Group updated consensus-based recommendations for management of the difficult airway: part 1. Difficult airway management encountered in an unconscious patient

2021· article· en· W3168446116 on OpenAlexaffabout
J. Adam Law, Laura V. Duggan, Mathieu Asselin, Paul Baker, Edward T. Crosby, Andrew Downey, Orlando Hung, Philip M. Jones, François Lemay, Rüdiger Noppens, Matteo Parotto, Roanne Preston, Kathryn Sparrow, Timothy P. Turkstra, David T. Wong, George Kovács

Bibliographic record

VenueCanadian Journal of Anesthesia/Journal canadien d anesthésie · 2021
Typearticle
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsToronto Western HospitalMemorial University of NewfoundlandB.C. Women's Hospital & Health CentreHôtel-Dieu de QuébecUniversity of TorontoWestern UniversityUniversité LavalUniversity of OttawaHôpital de l'Enfant-JésusUniversity Health NetworkOttawa HospitalHealth Sciences CentreToronto General HospitalDalhousie University
Fundersnot available
KeywordsMedicineAirwayAirway managementLaryngoscopyIntubationTracheal intubationIntensive care medicineMEDLINECINAHLLaryngeal MasksSystematic reviewLaryngeal mask airwayAnesthesiaPsychological interventionNursing

Abstract

fetched live from OpenAlex

PURPOSE: Since the last Canadian Airway Focus Group (CAFG) guidelines were published in 2013, the literature on airway management has expanded substantially. The CAFG therefore re-convened to examine this literature and update practice recommendations. This first of two articles addresses difficulty encountered with airway management in an unconscious patient. SOURCE: Canadian Airway Focus Group members, including anesthesia, emergency medicine, and critical care physicians, were assigned topics to search. Searches were run in the Medline, EMBASE, Cochrane Central Register of Controlled Trials, and CINAHL databases. Results were presented to the group and discussed during video conferences every two weeks from April 2018 to July 2020. These CAFG recommendations are based on the best available published evidence. Where high-quality evidence was lacking, statements are based on group consensus. FINDINGS AND KEY RECOMMENDATIONS: Most studies comparing video laryngoscopy (VL) with direct laryngoscopy indicate a higher first attempt and overall success rate with VL, and lower complication rates. Thus, resources allowing, the CAFG now recommends use of VL with appropriately selected blade type to facilitate all tracheal intubations. If a first attempt at tracheal intubation or supraglottic airway (SGA) placement is unsuccessful, further attempts can be made as long as patient ventilation and oxygenation is maintained. Nevertheless, total attempts should be limited (to three or fewer) before declaring failure and pausing to consider "exit strategy" options. For failed intubation, exit strategy options in the still-oxygenated patient include awakening (if feasible), temporizing with an SGA, a single further attempt at tracheal intubation using a different technique, or front-of-neck airway access (FONA). Failure of tracheal intubation, face-mask ventilation, and SGA ventilation together with current or imminent hypoxemia defines a "cannot ventilate, cannot oxygenate" emergency. Neuromuscular blockade should be confirmed or established, and a single final attempt at face-mask ventilation, SGA placement, or tracheal intubation with hyper-angulated blade VL can be made, if it had not already been attempted. If ventilation remains impossible, emergency FONA should occur without delay using a scalpel-bougie-tube technique (in the adult patient). The CAFG recommends all institutions designate an individual as "airway lead" to help institute difficult airway protocols, ensure adequate training and equipment, and help with airway-related quality reviews.

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)0.052
metaresearch head score (Gemma)0.127
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: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score0.499

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.127
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.007
Bibliometrics0.0180.011
Science and technology studies0.0050.003
Scholarly communication0.0040.004
Open science0.0110.005
Research integrity0.0100.011
Insufficient payload (model declined to judge)0.0070.003

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 head0.014
GPT teacher head0.239
Teacher spread0.225 · 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
GenreMethods

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

Citations169
Published2021
Admission routes2
Has abstractyes

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