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Record W2098035069 · doi:10.1097/aln.0000000000000848

Ultrasound Improves Cricothyrotomy Success in Cadavers with Poorly Defined Neck Anatomy

2015· article· en· W2098035069 on OpenAlexaff
Naveed Siddiqui, Cristián Arzola, Zeev Friedman, Laarni Guerina, Kong Eric You-Ten

Bibliographic record

VenueAnesthesiology · 2015
Typearticle
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsPalpationMedicineCricothyrotomyCadaverUltrasoundLarynxSurgeryAnatomyRadiologyAirwayAirway management

Abstract

fetched live from OpenAlex

AbstractAbstract This is the first study systematically assessing whether ultrasound guidance can reduce the complications associated with cricothyrotomy performed in human cadaver. The incidence of injuries to the larynx and trachea during ultrasoundguided cricothyrotomy was significantly lower compared with conventional digital palpation technique (25 vs . 74%), whereas the insertion time was significantly longer with ultrasound than with digital palpation (196 vs . 110 s). Results of this study suggest that ultrasound guidance of the cricothyroid membrane and neck landmarks should be performed before airway management, particularly in patients with difficult palpable neck landmarks and difficult airways. Background Misidentification of the cricothyroid membrane in a “cannot intubate-cannot oxygenate” situation can lead to failures and serious complications. The authors hypothesized that preprocedure ultrasound-guided identification of the cricothyroid membrane would reduce complications associated with cricothyrotomy. Methods A group of 47 trainees were randomized to digital palpation (n = 23) and ultrasound (n = 24) groups. Cricothyrotomy was performed on human cadavers by using the Portex ® device (Smiths Medical, USA). Anatomical landmarks of cadavers were graded as follows: grade 1—easy = visual landmarks; 2—moderate = requires light palpation of landmarks; 3—difficult = requires deep palpation of landmarks; and 4—impossible = landmarks not palpable. Primary outcome was the complication rate as measured by the severity of injuries. Secondary outcomes were correct device placement, failure to cannulate, and insertion time. Results Ultrasound guidance significantly decreased the incidence of injuries to the larynx and trachea (digital palpation: 17 of 23 = 74% vs. ultrasound: 6 of 24 = 25%; relative risk, 2.88; 95% CI, 1.39 to 5.94; P = 0.001) and increased the probability of correct insertion by 5.6 times ( P = 0.043) in cadavers with difficult and impossible landmark palpation (digital palpation 8.3% vs. ultrasound 46.7%). Injuries were found in 100% of the grades 3 to 4 (difficult–impossible landmark palpation) cadavers by digital palpation compared with only 33% by ultrasound ( P < 0.001). The mean (SD) insertion time was significantly longer with ultrasound than with digital palpation (196.1 s [60.6 s] vs. 110.5 s [46.9 s]; P < 0.001). Conclusion Preprocedure ultrasound guidance in cadavers with poorly defined neck anatomy significantly reduces complications and improves correct insertion of the airway device in the cricothyroid membrane.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.016
GPT teacher head0.273
Teacher spread0.257 · 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 designBench or experimental
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

Citations119
Published2015
Admission routes1
Has abstractyes

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