Airway ultrasound: Point of care in children—The time is now
Bibliographic record
Abstract
BACKGROUND: Point-of-care ultrasonography of the airway is becoming a first-line noninvasive adjunct assessment tool of the pediatric airway. It is defined as a focused and goal-directed portable ultrasonography brought to the patient and performed and interpreted on the spot by the provider. Successful use requires a thorough understanding of airway anatomy and ultrasound experience. AIMS: To outline the many benefits, and some limitations, of airway ultrasonography in the clinical and perioperative setting. MATERIALS AND METHODS: Expert review of the recent literature. RESULTS: Ultrasound assessment of the airway may provide the clinician with valuable information that is specific to the individual airway static and dynamic anatomy of the patient. Ultrasound can help identify vocal cord dysfunction and pathology, assess airway size, predict the appropriate diameter of endotracheal and tracheostomy tubes, differentiate tracheal from esophageal intubation, localize the cricothyroid membrane for emergency airway access and identify tracheal rings for US-guided tracheostomy. Ultrasonography is also a great tool for the intraoperative diagnosis of a pneumothorax, the visualization of the movement of the diaphragms, and quantifying the amount of gastric content. Ultrasonography signs, tips, and pearls that allow these diagnoses are highlighted. The major disadvantage of ultrasonography remains interobserver variability, and operator dependence, as it requires specific training and experience. CONCLUSION: Although it is not standard of care yet, there is significant potential for the integration of ultrasound technology into the routine care of the airway.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".