Orotracheal intubation incorporating aerosol-mitigating strategies by anaesthesiologists, intensivists and emergency physicians: a simulation study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it