MétaCan
Menu
Back to cohort
Record W4251110855 · doi:10.1213/ane.0000000000001606

In Response

2016· letter· en· W4251110855 on OpenAlexaffabout
Orlando Hung, J. Adam Law, Ian D. Morris, Michael Murphy

Bibliographic record

VenueAnesthesia & Analgesia · 2016
Typeletter
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsUniversity of AlbertaDalhousie University
Fundersnot available
KeywordsMedicineAirwayLaryngoscopyGlobeAirway managementIntubationSurgery

Abstract

fetched live from OpenAlex

Incomplete Airway Assessment We thank Dr. Nørskov et al1 for their thoughtful comments concerning our editorial.2 We also are grateful for their clarification of methods used for airway assessment in their Danish Anaesthesia Database study.1 Although it is clear that airway evaluation before intervention is a critical step to minimize adverse outcomes, we agree with Nørskov et al1 that there is ample room for improvement in the evaluation tools we have to date. In other words, because unanticipated difficulties in managing the airway will likely continue to occur in our clinical practice, airway practitioners must be equipped with a strategy to manage an unanticipated difficult or failed airway (ie, plan B, plan C).3–5 Recognizing that the survey mentioned in our editorial2 was an informal solicitation of information regarding the airway evaluation section of preanesthetic assessment forms (an image taken from a smartphone), there is potential for bias in our data collection. The countries involved in our survey included Australia, Canada, England, Germany, Italy, the Netherlands, New Zealand, Singapore, Rwanda, and the United States. As summarized in our editorial, the airway assessment section on these forms varied substantially, ranging from descriptive text only to a list of predictors of difficult direct laryngoscopy with Mallampati score, this latter, the most common predictor present on these preanesthetic assessment forms. On the basis of the information collected, we concluded that assessment for difficult direct laryngoscopy remains the main focus of airway assessment at many centers around the globe. The form from our center at Dalhousie University was the only one asking for predicted difficulty in bag-mask ventilation and surgical airway (cricothyrotomy). None of the forms surveyed queried assessment of predicted difficulty in using an extraglottic device, such as a laryngeal mask airway. We agree with Nørskov et al1 that we must find solutions to improve and correct problems related to incomplete airway assessment. In addition to assessing the patient for predicted difficulty with direct or video laryngoscopy, we must stress the importance of assessing difficulties in other methods in oxygenation and ventilation. These include difficulties in bag-mask ventilation, the use of an extraglottic device (eg, a laryngeal mask airway), and front-of-neck access (cricothyrotomy) so that appropriate airway management strategies can be planned before intervention. Adverse patient physiology (eg, full stomach, anticipated intolerance of apnea) with the potential to impact the choice of approach to the airway also must be considered. Orlando Hung, MD, FRCPCJ. Adam Law, MD, FRCPCIan Morris, MD, FRCPCDepartment of Anesthesia, Pain Management andPerioperativeMedicineDalhousie UniversityHalifax, Nova Scotia, Canada[email protected] Michael Murphy, MD, FRCPCDepartment of Anesthesiology and Pain MedicineUniversity of AlbertaEdmonton, Alberta, Canada

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)0.005
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.589
Threshold uncertainty score0.841

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.065
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.4110.202

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 head0.015
GPT teacher head0.273
Teacher spread0.258 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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

Citations0
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueAnesthesia & AnalgesiaSame topicAirway Management and Intubation TechniquesFrench-language works237,207