Patient barrier acceptance during airway management among anesthesiologists: a simulation pilot study
Bibliographic record
Abstract
BACKGROUND: Protection of healthcare providers (HCP) has been a serious challenge in the management of patients during the coronavirus 2019 (COVID-19) pandemic. Additional physical barriers have been created to enhance personal protective equipment (PPE). In this study, user acceptability of two novel barriers was evaluated and the performance of airway management using PPE alone versus PPE plus the additional barrier were compared. METHODS: An open-label, double-armed simulation pilot study was conducted. Each participant performed bag-mask ventilation and endotracheal intubation using a GlideScope in two scenarios: 1) PPE donned, followed by 2) PPE donned plus the addition of either the isolation chamber (IC) or aerosol box (AB). Endotracheal intubation using videolaryngoscopy was timed. Participants completed pre- and post-simulation questionnaires. RESULTS: Twenty-nine participants from the Department of Anesthesia were included in the study. Pre- and post-simulation questionnaire responses supported the acceptance of additional barriers. There was no significant difference in intubating times across all groups (PPE vs. IC 95% CI, 26.3, 35.1; PPE vs. AB 95% CI, 25.9, 35.5; IC vs. AB 95% CI, 23.6, 39.1). Comparison of post-simulation questionnaire responses between IC and AB showed no significant difference. Participants did not find the additional barriers negatively affected communication, visualization, or maneuverability. CONCLUSIONS: Overall, the IC and AB were comparable, and there was no negative impact on performance under testing conditions. Our study suggests the positive acceptance of additional patient protection barriers by anesthesia providers during airway management.
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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 | 0.004 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".