Helium-Oxygen Mixture Bag, a Novel Way for Extubation of COVID-19 Patients
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19) has been a worldwide pandemic in 2020; necessitating significant changes in patient-care procedures. Because of the risk of transmission to health care workers (HCWs) and the shortage of personal protective devices worldwide, novel protective barriers during aerosol-generating procedures have been developed. The intubation box has been proposed and gained popularity. A safe way for extubating patients with COVID-19 in critical care settings does not exist. This report discusses the development and assessment of the efficacy of using a Helium-Oxygen mixture (Heliox) filled bag during the extubation phase for the protection of HCW. Study Design and Methods: This methodology was developed at two tertiary care hospitals in Riyadh, Saudi Arabia and Toronto, Canada. We describe a novel way using a bag filled with Heliox bag. We performed extubation of an intubated manikin with and without using the Heliox bag. The cough during extubation was simulated using a fluorescent dye-filled balloon, which was inflated with a hidden oxygen tube until it bursts. We used an ultraviolet (UV) light source to assess the aerosols generated during extubation. Results: During extubation using the Heliox bag, droplets of the fluorescent dye were all contained within the Heliox bag and only found on the manikin chest. While during extubation without using Heliox bag, using the UV light, we found droplets of the fluorescent dye on the HCW mask and hand, the bed, the floor, and wall of the room. Conclusion: In our simulated experiment, we found that the Heliox bag is an easy and reproducible way for extubating patients with COVID-19 and any other airborne disease. We also found that the Heliox bag is an effective way to protect HCW.
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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 | 0.000 | 0.004 |
| 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) | 0.001 | 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 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".