Emergency front‐of‐neck access in pediatric anesthesia: A narrative review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Children undergoing airway management during general anesthesia may experience airway complications resulting in a rare but life-threatening situation known as "Can't Intubate, Can't Oxygenate". This situation requires immediate recognition, advanced airway management, and ultimately emergency front-of-neck access. The absence of standardized procedures, lack of readily available equipment, inadequate knowledge, and training often lead to failed emergency front-of-neck access, resulting in catastrophic outcomes. In this narrative review, we examined the latest evidence on emergency front-of-neck access in children. METHODS: A comprehensive literature was performed the use of emergency front-of-neck access (eFONA) in infants and children. RESULTS: Eighty-six papers were deemed relevant by abstract. Finally, eight studies regarding the eFONA technique and simulations in animal models were included. For all articles, their primary and secondary outcomes, their specific animal model, the experimental design, the target participants, and the equipment were reported. CONCLUSION: Based on the available evidence, we propose a general approach to the eFONA technique and a guide for implementing local protocols and training. Additionally, we introduce the application of innovative tools such as 3D models, ultrasound, and artificial intelligence, which can improve the precision, safety, and training of this rare but critical procedure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".