Orotracheal intubation incorporating aerosol-mitigating strategies by anaesthesiologists, intensivists and emergency physicians: a simulation study
Bibliographic record
Abstract
Background: Orotracheal intubation (OTI) can result in aerosolisation leading to an increased risk of infection for healthcare providers, a key concern during the COVID-19 pandemic. Objective: This study aimed to evaluate the OTI time and success rate of two aerosol-mitigating strategies under direct laryngoscopy and videolaryngoscopy performed by anaesthesiologists, intensive care physicians and emergency physicians who were voluntarily recruited for OTI in an airway simulation model. Methodology: The outcomes were successful OTI, degree of airway visualisation and time required for OTI. Not using a stylet during OTI reduced the success rate among non-anaesthesiologists and increased the time required for intubation, regardless of the laryngoscopy device used. Results: Success rates were similar among physicians from different specialties during OTI using videolaryngoscopy with a stylet. The time required for successful OTI by intensive care and emergency physicians using videolaryngoscopy with a stylet was longer compared with anaesthesiologists using the same technique. Videolaryngoscopy increased the time required for OTI among intensive care physicians compared with direct laryngoscopy. The aerosol-mitigating strategy under direct laryngoscopy with stylet did not increase the time required for intubation, nor did it interfere with OTI success, regardless of the specialty of the performing physician. Conclusions: The use of a stylet within the endotracheal tube, especially for non-anaesthesiologists, had an impact on OTI success rates and decreased procedural time.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".