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Record W3171130740 · doi:10.1007/s12630-021-02008-z

Canadian Airway Focus Group updated consensus-based recommendations for management of the difficult airway: part 2. Planning and implementing safe management of the patient with an anticipated difficult airway

2021· article· en· W3171130740 on OpenAlexaffabout
J. Adam Law, Laura V. Duggan, Mathieu Asselin, Paul Baker, Edward T. Crosby, Andrew Downey, Orlando Hung, George Kovács, François Lemay, Rüdiger Noppens, Matteo Parotto, Roanne Preston, Kathryn Sparrow, Timothy P. Turkstra, David T. Wong, Philip M. Jones

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 CentreUniversity of TorontoWestern UniversityHôtel-Dieu de QuébecUniversité LavalUniversity of OttawaHôpital de l'Enfant-JésusUniversity Health NetworkOttawa HospitalHealth Sciences CentreToronto General HospitalDalhousie University
Fundersnot available
KeywordsAirwayMedicineAirway managementIntubationLaryngoscopyMEDLINEEmergency departmentIntensive care medicineAnesthesiaNursing

Abstract

fetched live from OpenAlex

PURPOSE: Since the last Canadian Airway Focus Group (CAFG) guidelines were published in 2013, the published airway management literature has expanded substantially. The CAFG therefore re-convened to examine this literature and update practice recommendations. This second of two articles addresses airway evaluation, decision-making, and safe implementation of an airway management strategy when difficulty is anticipated. 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 is lacking, statements are based on group consensus. FINDINGS AND KEY RECOMMENDATIONS: Prior to airway management, a documented strategy should be formulated for every patient, based on airway evaluation. Bedside examination should seek predictors of difficulty with face-mask ventilation (FMV), tracheal intubation using video- or direct laryngoscopy (VL or DL), supraglottic airway use, as well as emergency front of neck airway access. Patient physiology and contextual issues should also be assessed. Predicted difficulty should prompt careful decision-making on how most safely to proceed with airway management. Awake tracheal intubation may provide an extra margin of safety when impossible VL or DL is predicted, when difficulty is predicted with more than one mode of airway management (e.g., tracheal intubation and FMV), or when predicted difficulty coincides with significant physiologic or contextual issues. If managing the patient after the induction of general anesthesia despite predicted difficulty, team briefing should include triggers for moving from one technique to the next, expert assistance should be sourced, and required equipment should be present. Unanticipated difficulty with airway management can always occur, so the airway manager should have a strategy for difficulty occurring in every patient, and the institution must make difficult airway equipment readily available. Tracheal extubation of the at-risk patient must also be carefully planned, including assessment of the patient's tolerance for withdrawal of airway support and whether re-intubation might be difficult.

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)0.084
metaresearch head score (Gemma)0.168
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 score0.788
Threshold uncertainty score0.477

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0840.168
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.008
Bibliometrics0.0170.011
Science and technology studies0.0060.003
Scholarly communication0.0050.005
Open science0.0120.006
Research integrity0.0100.012
Insufficient payload (model declined to judge)0.0080.004

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 head0.017
GPT teacher head0.250
Teacher spread0.232 · 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

Citations188
Published2021
Admission routes2
Has abstractyes

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