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Record W4281738670 · doi:10.1097/aco.0000000000001128

Safe in the first attempt: teaching neonatal airway management

2022· review· en· W4281738670 on OpenAlexaff
Annery G. García‐Marcinkiewicz, Clyde Matava

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2022
Typereview
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineAirwayAirway managementIntensive care medicineIntubationTracheal intubationVentilation (architecture)PopulationAnesthesia

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Quick and precise facemask ventilation and tracheal intubation are critical clinical skills in neonatal airway management. In addition, this vulnerable population requires a thorough understanding of developmental airway anatomy and respiratory physiology to manage and anticipate potential airway mishaps. Neonates have greater oxygen consumption, increased minute ventilation relative to functional residual capacity, and increased closing volumes compared to older children and adults. After a missed airway attempt, this combination can quickly lead to dire consequences, such as cardiac arrest. Keeping neonates safe throughout the first attempt of airway management is key. RECENT FINDINGS: Several techniques and practices have evolved to improve neonatal airway management, including improvement in neonatal airway equipment, provision of passive oxygenation, and closer attention to the management of anesthetic depth. The role of nontechnical skills during airway management is receiving more recognition. SUMMARY: Every neonatal intubation should be considered a critical event. Below we discuss some of the challenges in neonatal airway management, including anatomical and physiological principles which must be understood to approach the airway. We then follow with a description of current evidence for best practices and training.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.115
GPT teacher head0.404
Teacher spread0.289 · 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.

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

Citations5
Published2022
Admission routes1
Has abstractyes

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