Pediatric airway management
Bibliographic record
Abstract
PURPOSE OF REVIEW: Children are at risk of severe hypoxemia in the perioperative period owing to their unique anatomy and physiology. Safe and effective airway management strategies are therefore key to the practice of pediatric anesthesia. The goal of this review is to highlight recent publications (2019-2021) aimed to advance pediatric airway safety and to highlight a proposed simple, pediatric-specific, universal framework to guide clinical practice. RECENT FINDINGS: Recent investigations demonstrate that infants with normal and difficult airways experience high incidences of multiple laryngoscopy attempts and resulting hypoxemia. Video laryngoscopy may improve tracheal intubation first attempt success rate in infants with normal airways. In infants with difficult airways, standard blade video laryngoscopy is associated with higher first attempt success rates over non-standard blade video laryngoscopy. Recent studies in children with Pierre Robin sequence and mucopolysaccharidoses help guide airway equipment and technique selection. Department airway leads and hospital difficult airway services are necessary to disseminate knowledge, lead quality improvement initiatives, and promote evidence-based practice guidelines. SUMMARY: Pediatric airway management morbidity is a common problem in pediatric anesthesia. Improvements in individual practitioner preparation and management strategies as well as systems-based policies are required. A simple, pediatric-specific, universal airway management framework can be adopted for safe pediatric anesthesia practice.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".