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Record W2748682004 · doi:10.1111/anae.13955

Category‐1 caesarean section, airways and Julius Caesar. A reply

2017· letter· en· W2748682004 on OpenAlexaff
Aaron J. Krom, Yoram Cohen, Tiberiu Ezri, Stephen H. Halpern, J. Philip Miller, Yehuda Ginosar

Bibliographic record

VenueAnaesthesia · 2017
Typeletter
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineCaesarean sectionIntubationProcess (computing)BackupIntensive care medicineVentilation (architecture)Airway managementPregnancyAnesthesiaComputer science

Abstract

fetched live from OpenAlex

We thank Sorbello and Micaglio for their comments. We agree that pregnant patients present unique challenges for airway management 1. In our study on anaesthesia management for emergency caesarean delivery in women with predicted difficult intubation/ventilation 2, literature searches focused on data sources exclusively from obstetric patients. However, in some nodes on the decision tree, obstetric data was unavailable and we had no alternative but to make some assumptions and extrapolate data from non-obstetric situations (e.g. obese patients). It is likely that the true (unknown) obstetric data would be somewhat different, but based on our sensitivity analysis 2, this would not impact greatly on the final outcome of our study. Yentis wrote that while decision analysis “involves many assumptions and estimations” it “emphasises the role of using such evidence as exists in a more structured and focused way” 3. By contrast, he stated that the traditional decision making process “relies purely on assumptions and estimations–worse, there is no structured incorporation of evidence or estimation of actual likelihoods at all. Instead the process is entirely intuitive”. In light of these remarks, we challenge both clinical assertions made by Sorbello and Micaglio, firstly that “regional anaesthesia should not even be attempted if difficult ventilation is anticipated”, and secondly, that “awake technique is preferable if there is anticipated difficult ventilation”. We do not agree that regional anaesthesia should not be attempted. In fact, most obstetric anaesthetists would prefer regional anaesthesia under these circumstances if time permitted and appropriate backup equipment was available 4. Indeed, the most important contribution of epidural labour analgesia to maternal safety has been the ability to rapidly and safely convert epidural labour analgesia to surgical anaesthesia for unscheduled caesarean delivery. This advantage is particularly beneficial in mothers with predicted difficult intubation and difficult ventilation. We agree that rapid sequence induction with videolaryngoscopy (RSI-VL) is not ideal 5, but the scenario under discussion involved a time-critical category-1 caesarean section. The key message of our paper is that the time for successful airway management in RSI-VL was 100 (87–114) s, vs. 9 min for awake fibreoptic intubation and 6.3 min for rapid spinal. The risks of RSI-VL are relatively low (21 incidents per 100,000), and we consider them to be acceptable if slower techniques would entail much greater long-term risks to the fetus. Although we disagree with Sorbello and Micaglio, such disagreement is legitimate; maybe their intuition and experience are different from ours. This is exactly why an attempt to obtain and marshal literature-based evidence is so important. Where direct evidence is lacking and where the question is not amenable to a randomised clinical trial, decision analysis may provide a literature-based estimation of likely outcomes. Otherwise, to echo their quote from Julius Caesar, ‘libenter homines id quod volunt credunt’ (man will readily believe what he wishes to be true).

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 categoriesMeta-epidemiology (narrow)
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 score0.206
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.266
Teacher spread0.245 · 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.

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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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