A comparison of droplet and contact contamination using 3 simulated barrier techniques for COVID-19 intubation: a quality assurance study
Bibliographic record
Abstract
BACKGROUND: The intubation of patients with coronavirus disease 2019 (COVID-19) puts health care workers at risk of infection through aerosol, droplet and contact contamination. We evaluated the risk of droplet and contact contamination for health care workers using 3 intubation barrier techniques as part of a quality assurance study at our institution. METHODS: This randomized quality assurance study was completed at a tertiary academic hospital in Vancouver, British Columbia, Canada, on Apr. 4, 2020. Participants in personal protective equipment performed simulated intubations on a manikin with (a) no barrier, (b) a clear plastic sheet covering the manikin and (c) a plexiglass intubation box over the manikin, in random order. Fluorescein was ejected from inside the manikin's mouth to simulate droplet and contact spread during a standard intubation sequence. Two blinded independent assessors evaluated the location and degree of contamination on the intubator and assistant using an ultraviolet light. Contamination severity was rated in a standard fashion (0 = none; 1 = minor; 2 = major). The primary outcome was total contamination score and secondary outcomes were scores between intubator and assistant, anatomic areas contaminated and qualitative feedback on ease of intubation. RESULTS: = 0.2). For the intubator, the total contamination score was higher when the sheet was used (median 29 [interquartile range (IQR) 25-34]) than when the box was used (median 17 [IQR 15-22]) or when no barrier was used (median 18 [IQR 13-21]). All 5 participants reported challenges during intubation using the sheet. INTERPRETATION: Use of a plastic sheet while intubating patients with COVID-19 may increase the risk of droplet and contact contamination during intubation and impede intubation. Further study should be undertaken before implementing barrier techniques in practice.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".