Gravity steam reprocessing in healthcare facilities for the reuse of N95 respirators
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) has significantly impacted the health of millions of people around the world. The shortage of personal protective equipment, including N95 respirators, in hospital facilities has put frontline healthcare professionals at high risk for contracting this virus. AIM: To develop a reproducible and safe N95 respirator reprocessing method that satisfies all presented regulatory standards and that can be directly implemented by hospitals using existing available equipment. METHODS: A non-toxic gravity steam reprocessing method has been developed for the reuse of N95 respirators consisting of 30 min of steam treatment at 121°C followed by 30 min of heat drying. Samples of model number 1860, 1860s, 1870+, and 9105 N95 respirators were either collected from hospitals (for microbiology testing) or purchased new (for functionality testing), with all functionality tests (i.e. filter efficiency, fit evaluation, and strap integrity) performed at the Centers for Disease Control and Prevention using standard procedures established by the National Institute for Occupational Safety and Health. FINDINGS: All tested models passed the minimum filter efficiency of 95% after three cycles of gravity steam reprocessing. The 1870+ N95 respirator model is the most promising model for reprocessing based on its efficient bacterial inactivation coupled with the maintenance of all other key functional respirator properties after multiple reprocessing steps. CONCLUSIONS: The gravity steam method can effectively reprocess N95 respirators over at least three reprocessing cycles without negatively impacting the functionality requirements set out by regulators. Enabling the reuse of N95 respirators is a crucial tool for managing both the current pandemic and future healthcare crises.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 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".