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Record W2746697313 · doi:10.1111/anae.14014

Current practice for awake fibreoptic intubation – asking the right questions

2017· letter· en· W2746697313 on OpenAlexaboutno aff
Kariem El‐Boghdadly, Desire N. Onwochei, J. Cuddihy, Imran Ahmad

Bibliographic record

VenueAnaesthesia · 2017
Typeletter
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)MedicineAuditSightManagementHistory

Abstract

fetched live from OpenAlex

We would like to thank Drs. Murphy and Howes for their thoughtful editorial, which accompanied our recent study of awake fibreoptic intubation (AFOI) practice 1, 2, and for recognising the training opportunities that our institution provides. However, rather than alluding to unanswered questions in our data, we are concerned that they have applied conjecture and inference to ask the wrong questions about AFOI. Murphy and Howes assert that ‘placing particular emphasis on any individual component … risks losing sight of the bigger picture’. Just as pilots emphasise training on the most critical phases of flight, so too must clinicians. Efforts must be made to train for complex, procedural skills as well as considering the important, but non-specific, ‘bigger picture’. The question is – how can we excel at all components of the airway management pathway? The simple answer is self-evident in our data: training. Murphy and Howes cite an editorial 3 written by one of our authors. Our prospective study (rather than audit, as there are no accepted standards) has demonstrated that AFOI is associated with low morbidity and a high success rate, particularly when appropriate training is undertaken. Although we do not state that AFOI should be the ‘gold standard’, our data clearly highlights that AFOI has a valuable role to play in the management of the difficult airway. Had Drs. Murphy and Howes put Ahmad and Bailey's editorial into context 3, they would have understood that training is recommended for AFOI, and when not undertaken, AFOI should be considered a specialist skill. Performed by appropriately trained and competent clinicians, the utility of a technique that has been part of anaesthetic practice for 50 years is difficult to refute. The question here, then, is: who should train in AFOI? We thank the authors for contextualising our prospective data with retrospective results collected in the USA 4 and Canada 5. Retrospective data points can be under-reported, and the low complication rates reported by Joseph et al. 4 could be an inaccurate representation of their true incidence. Moreover, the comparable complication rates provided by Law et al. 5 might also be under-reported. It would be interesting to know what prospective data from North America shows. Comparing our results with data from unsedated, healthy course delegates (a self-selected group of subjects) is misleading 6. The immediate complication rate in healthy volunteers was greater than we found in comorbid patients with complex airway pathology (19.5% vs. 11% respectively), and so the co-administration of sedation does not necessarily correlate with increased risk. Interestingly, the Difficult Airway Society has recently commissioned national guidelines on the performance of AFOI. Whether sedation will be recommended as standard practice remains to be seen. The question that needs answering here is: does sedation increase or decrease the safety of performing AFOI? We agree that there is insufficient evidence recommending high-flow nasal oxygen (HFNO) for all AFOIs. Murphy and Howes infer our data do not demonstrate that HFNO increases safety, and point to an interesting study showing that HFNO increases the time to intubation during rapid sequence induction (RSI) without increasing desaturation rates, despite the primary aim of RSI being to rapidly secure the airway 7. We believe that this data mirrors ours; despite increased sedation, we found no difference in the rates of desaturation. This is a positive finding for HFNO techniques, and should be viewed as improving the evidence-base for administering HFNO, but requires further research.

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)0.001
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.225
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.337
Teacher spread0.309 · 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
GenreCommentary

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
Published2017
Admission routes1
Has abstractyes

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