Standard hospital blanket warming cabinets can be utilized for complete moist heat SARS-CoV2 inactivation of contaminated N95 masks for re-use
Bibliographic record
Abstract
Shortages of personal protective equipment for use during the SARS-CoV-2 pandemic continue to be an issue among health-care workers globally. Extended and repeated use of N95 filtering facepiece respirators without adequate decontamination is of particular concern. Although several methods to decontaminate and re-use these masks have been proposed, logistic or practical issues limit adoption of these techniques. In this study, we propose and validate the use of the application of moist heat (70 °C with humidity augmented by an open pan of water) applied by commonly available hospital (blanket) warming cabinets to decontaminate N95 masks. This report shows that a variety of N95 masks can be repeatedly decontaminated of SARS-CoV-2 over 6 h moist heat exposure without compromise of their filtering function as assessed by standard fit and sodium chloride aerosol filtration efficiency testing. This approached can easily adapted to provide point-of-care N95 mask decontamination allowing for increased practical utility of mask recycling in the health care setting.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".