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Record W2341264237 · doi:10.1213/ane.0000000000001258

Predictors of Difficult Intubation with the Bonfils Rigid Fiberscope

2016· article· en· W2341264237 on OpenAlexaff
Michał Nowakowski, Stephan Williams, Jason R. Gallant, Monique Ruel, Arnaud Robitaille

Bibliographic record

VenueAnesthesia & Analgesia · 2016
Typearticle
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineIntubationLaryngoscopyAnesthesiaRapid sequence inductionGlottisAirwayStyletFiberscopeSurgeryCricoid cartilageLarynx

Abstract

fetched live from OpenAlex

BACKGROUND: Endotracheal intubation is commonly performed via direct laryngoscopy (DL). However, in certain patients, DL may be difficult or impossible. The Bonfils Rigid Fiberscope® (BRF) is an alternative intubation device, the design of which raises the question of whether factors that predict difficult DL also predict difficult BRF. We undertook this study to determine which demographic, morphologic, and morphometric factors predict difficult intubation with the BRF. METHODS: Four hundred adult patients scheduled for elective surgery were recruited. Patients were excluded if awake intubation, rapid sequence induction, or induction without neuromuscular blocking agents was planned. Data were recorded, including age, sex, weight, height, American Society of Anesthesiologist classification, history of snoring and sleep apnea, Mallampati class, upper lip bite test score, interincisor, thyromental and sternothyroid distances, manubriomental distances in flexion and extension, neck circumference, maximal neck flexion and extension, neck skinfold thickness at the cricoid cartilage, and Cormack and Lehane grade obtained via DL after paralysis was confirmed. Quality of glottic visualization (good or poor), as well as the number of intubation attempts and time to successful intubation with the BRF, was noted. Univariate analyses were performed to evaluate the association between patient characteristics and time required for intubation. Variables that exhibited a significant correlation were included in a multivariate analysis using a standard least squares model. A P < 0.05 was considered significant. RESULTS: Glottic visualization with the BRF was good in 396 of 400 (99%) cases. On the first attempt, 390 patients were successfully intubated with the BRF; 6 patients required >1 attempt; 4 patients could not be intubated by using the BRF alone. These 4 patients were intubated by using a combination of DL and BRF (2 patients), DL and a Frova bougie (1 patient), and DL and an endotracheal tube shaped with a semirigid stylet (1 patient). Mean time for successful intubation was 26 ± 13 seconds. Multivariate analysis showed that decreased mouth opening (P = 0.008), increased body mass index (P = 0.011), and higher Cormack and Lehane grade (P = 0.038) predicted longer intubation times, whereas shorter thyromental distance predicted slightly shorter intubation times (P < 0.0001). CONCLUSIONS: Mouth opening, body mass index, and high Cormack and Lehane grade predict longer intubation times, as with DL. Decreasing thyromental distance predicts slightly shorter intubation times with the BRF, possibly because of a design initially optimized for a pediatric population with receding chins. These findings, along with the high success rate of BRF in this study, and the possibility of further increasing success rates by combining BRF with DL, help define the role of BRF intubation in contemporary airway management.

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)0.000
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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 designObservational
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".

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Citations20
Published2016
Admission routes1
Has abstractyes

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