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Record W2913102475 · doi:10.1097/aln.0000000000002555

Preparation for and Management of “Failed” Laryngoscopy and/or Intubation

2019· article· en· W2913102475 on OpenAlexafffund
Richard M. Cooper

Bibliographic record

VenueAnesthesiology · 2019
Typearticle
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsUniversity Health Network
FundersUniversity of Texas MD Anderson Cancer CenterDalhousie University
KeywordsMedicineLaryngoscopyIntubationAirway managementAirwayTracheal intubationAnesthesiaAirway obstructionHypoxia (environmental)Ventilation (architecture)Tracheal tubeSupraglottic airwayOxygenationSurgery

Abstract

fetched live from OpenAlex

An airway manager's primary objective is to provide a path to oxygenation. This can be achieved by means of a facemask, a supraglottic airway, or a tracheal tube. If one method fails, an alternative approach may avert hypoxia. We cannot always predict the difficulties with each of the methods, but these difficulties may be overcome by an alternative technique. Each unsuccessful attempt to maintain oxygenation is time lost and may incrementally increase the risk of hypoxia, trauma, and airway obstruction necessitating a surgical airway. We should strive to optimize each effort. Differentiation between failed laryngoscopy and failed intubation is important because the solutions differ. Failed facemask ventilation may be easily managed with an supraglottic airway or alternatively tracheal intubation. When alveolar ventilation cannot be achieved by facemask, supraglottic airway, or tracheal intubation, every anesthesiologist should be prepared to perform an emergency surgical airway to avert disaster.

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)0.001
metaresearch head score (Gemma)0.004
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.002

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 head0.016
GPT teacher head0.316
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
GenreEmpirical

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

Citations28
Published2019
Admission routes2
Has abstractyes

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