Emergency airway management in the prone position: an observational mannequin-based simulation study
Bibliographic record
Abstract
INTRODUCTION: Accidental extubation during prone position can be a life-threatening emergency requiring rapid establishment of the airway. However, there is limited evidence of the best airway rescue method for this potentially catastrophic emergency. The aim of this study was to determine the most effective method to recover the airway in case of accidental extubation during prone positioning by comparing three techniques (supraglottic airway, video laryngoscopy, and fiber-optic bronchoscopy) in a simulated environment. METHODS: Eleven anesthesiologists and 12 anesthesia fellows performed the simulated airway management using 3 different techniques on a mannequin positioned prone in head pins. Time required for definitive airway management and the success rates were measured. RESULTS: The success rates of airway rescue were 100% with the supraglottic airway device (SAD), 69.6% with the video laryngoscope (CMAC), and 91.3% with the FOB. The mean (SD) time to insertion was 18.1 (4.8) s for the supraglottic airway, 78.3 (32.0) s for the CMAC, and 57.3 (24.6) s for the FOB. There were significant differences in the time required for definitive airway management between the SAD and FOB (t = 5.79, p < 0.001, 95% CI = 25.92-52.38), the SAD and CMAC (t = 8.90, p < 0.001, 95% CI = 46.93-73.40), and the FOB and CMAC (t = 3.11, p = 0.003, 95% CI = 7.78-34.25). CONCLUSION: The results of this simulation-based study suggest that the SAD I-gel is the best technique to manage accidental extubation during prone position by establishing a temporary airway with excellent success rate and shorter procedure time. When comparing techniques for securing a definitive airway, the FOB was more successful than the CMAC.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".