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Record W3155581274 · doi:10.1213/ane.0000000000005298

Airway Management in Surgical Patients With Obstructive Sleep Apnea

2021· review· en· W3155581274 on OpenAlexaff
Edwin Seet, Mahesh Nagappa, David T. Wong

Bibliographic record

VenueAnesthesia & Analgesia · 2021
Typereview
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsToronto Western HospitalUniversity of TorontoLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineObstructive sleep apneaAirwayIntubationAirway managementPerioperativeAnesthesiaIntensive care medicineVentilation (architecture)Sleep apneaAnesthesiology

Abstract

fetched live from OpenAlex

Obstructive sleep apnea (OSA) is the most common sleep-related breathing disorder, and the difficult airway is perhaps the anesthesiologists' quintessential concern. OSA and the difficult airway share certain similar anatomical, morphological, and physiological features. Individual studies and systematic reviews of retrospective, case-control, and large database studies have shown a likely association between patients with OSA and the difficult airway; OSA patients have a 3- to 4-fold higher risk of difficult intubation, difficult mask ventilation, or a combination of both. The presence of OSA should initiate proactive perioperative management in anticipation of a difficult airway. Prudent intraoperative management comprises the use of regional anesthesia where possible and considering an awake intubation technique where there is the presence of notable difficult airway predictors and risk of rapid desaturation following induction of general anesthesia. Familiarity with difficult airway algorithms, cautious extubation, and appropriate postoperative monitoring of patients with OSA are necessary to mitigate perioperative risks.

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)0.003
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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.284
Teacher spread0.268 · 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
GenreReview

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

Citations24
Published2021
Admission routes1
Has abstractyes

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