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Record W2774025394 · doi:10.24200/imminv.v2i4.112

Difficult airway management with a King Vision Video Laryngoscope in an anticipated patient and an unexpected patient: two scenarios, one device

2018· article· en· W2774025394 on OpenAlexaboutno aff
Eugenio Martínez Hurtado, Míriam Sánchez-Merchante, Javier Ripollés‐Melchor

Bibliographic record

VenueInternal Medicine And Medical Investigation Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsAirwayAirway managementMedicineIntubationVideo laryngoscopeLaryngoscopesPresentation (obstetrics)Case presentationMedical emergencyTracheal intubationIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

Background: The King Vision Video Laryngoscope is a relatively new device that has been incorporated in our daily surgical practice, intensive care unit, and remote areas. It has become one of the main alternatives to the rescue of a failed intubation, a tool to manage patients with difficult intubation predictors, and the first choice in ventilate and not-intubate situations.Case Presentation: In this case report, we present the management of two difficult airway cases: one in an induced patient and the other in an anticipated patient, according to the Canadian Airway Focus Group difficult airway recommendations.Conclusion: The King Vision Video Laryngoscope is effective in most adult patients and can be used with a mouth opening of at least 13 mm. Even is an effective dispositive, it has yet to show results in the management both conventional airway both difficult airway in routine clinical practice.

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)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0030.005
Open science0.0020.003
Research integrity0.0110.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.029
GPT teacher head0.331
Teacher spread0.301 · 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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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