MétaCan
Menu
Back to cohort
Record W2276518831 · doi:10.1111/anae.13343

A comparison of the Miller laryngoscope versus the prototype neonatal offset‐blade laryngoscope in a manikin

2015· article· en· W2276518831 on OpenAlexafffund
Srinivasa Murthy Doreswamy, C. Fusch, Ravi Selvaganapathy, Harpreet Matharoo, Sandesh Shivananda

Bibliographic record

VenueAnaesthesia · 2015
Typearticle
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsMcMaster University
FundersOntario Centres of Excellence
KeywordsMedicineIntubationAirwayAnesthesiaSurgery

Abstract

fetched live from OpenAlex

Laryngoscope blades used to intubate newborn babies are relatively bulky and frequently exert high pressure on the upper jaw. We tested a prototype neonatal offset-blade laryngoscope (NOBL) developed to overcome these limitations. Our aims were to compare the pressure on the upper jaw exerted by a size 0 Miller laryngoscope and the NOBL on a neonatal manikin, as well as the time taken to intubate the trachea and the area of view of the larynx. Twenty healthcare professionals with more than five years of experience in neonatal intensive care took part; the findings were assessed using pressure-sensitive film and photographs. High-pressure indentation occurred in 17 (85%) attempts using the Miller versus 1 (5%) using the NOBL (p = 0.0001). The median (IQR [range]) pressure exerted with the Miller laryngoscope was 455 (350-526 [75-650]) kPa vs 80 (0-133 [0-195]) kPa with the NOBL (p < 0.0001). The area of pressure exerted with the Miller laryngoscope was 68 (32-82 [0-110]) mm(2) vs 8 (0-23 [0-40]) mm(2) with the NOBL (p < 0.0001). The time to intubate was 8.3 (7.3-10.1[4-19]) s for the Miller and 8.0 (5.6-9.6 [4-13.5]) s for the NOBL (p < 0.0001). The area of view blocked by the Miller laryngoscope was 38% of the oral orifice versus 12% with the NOBL. We conclude that the NOBL significantly reduced undesired pressure on the upper jaw during tracheal intubation and improved the view of the larynx compared with a conventional laryngoscope.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

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

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 head0.067
GPT teacher head0.350
Teacher spread0.283 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueAnaesthesiaSame topicAirway Management and Intubation TechniquesFrench-language works237,207