A Rapidly Deployable Negative Pressure Enclosure for Aerosol-Generating Medical Procedures
Bibliographic record
Abstract
Abstract Background The coronavirus disease 2019 (COVID-19) pandemic presents significant safety challenges to healthcare professionals. In some jurisdictions, over 10% of confirmed cases of COVID-19 have been found among healthcare workers. Aerosol-generating medical procedures (AGMPs) may increase the risk of nosocomial transmission, exacerbated by present global shortages of personal protective equipment (PPE). Improved methods for mitigating risk during AGMPs are therefore urgently needed. Methods The Aerosol Containment Enclosure (ACE) was constructed from acrylic with silicone gaskets for arm port seals and completed with a thin plastic sheet. Hospital wall suction generated negative pressure within the ACE. To evaluate protective capability, differential pressures were recorded under static conditions and during simulated AGMPs. Smoke flow patterns, fluorescence aerosolization, and sodium saccharin aerosolization tests were also conducted. Results Negative pressures of up to -47.7 mmH 2 O were obtained using the enclosure with two wall suction units (combined outflow of 70 L min -1 ), with inflow of O 2 of 15 L min -1 . Negative pressures between -10 and - 35 mmH 2 O were maintained during simulated AGMPs, including oxygen delivery by mask, airway suctioning, bag-mask manual ventilation and endotracheal intubation of a potential COVID-19 patient. The ACE effectively contained smoke, fluorescein aerosol, and sodium saccharin aerosol within the enclosure during use. Conclusions The ACE is capable of maintaining negative pressure during simulated AGMPs. In all cases, containment was improved relative to an identical enclosure with non-occluded ports at ambient pressure. During the current COVID-19 pandemic, the use of such a device may assist in reducing nosocomial infections among healthcare providers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".