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 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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".