Comparative evaluation of four hydrogen peroxide-based systems to decontaminate N95 respirators
Bibliographic record
Abstract
Abstract Objective: Protocols designed to facilitate N95 filtering facepiece respirator (FFR) decontamination by commercial sterilization devices do not recommend that operators verify the device’s performance against pathogens deposited on FFRs. Here, we compared the treatment efficacy of 4 hydrogen peroxide-based systems that were authorized for N95 decontamination during the COVID-19 pandemic. Methods: Suspensions prepared from S. aureus ATCC 29213 and 44300, B. subtilis ATCC 6633, a vancomycin-resistant E. faecium isolate (VRE), E. coli ATCC 25922, and P. aeruginosa ATCC 27853 colonies were inoculated onto nine 1-cm 2 areas on a 3M 1805, 1860, 1860S, 1870+, 8210, 8110S, or 9105S FFR. Contaminated respirators were treated according to protocols recommended by the STERRAD 100NX, Bioquell Z-2, Sterizone VP4, or Clēan Works Mini systems. Decontamination efficacy was determined by comparing colony counts cultured from excised segments of treated and untreated FFR. Results: All devices achieved a 6-log reduction in bacterial burden and met FDA sterilization criteria. The Bioquell Z-2 device demonstrated 100% efficacy against both gram-positive and gram-negative organisms with all FFRs tested. Colonies of S. aureus ATCC 29213 and 44300 and VRE were cultivable from up to 9 (100%) of 9 STERRAD 100NX– and Sterizone VP4–treated segments. Viable B. subtilis ATCC 6633 organisms were recovered from 76.0% of STERRAD 100NX–treated FFR segments. Conclusions: Variability in decontamination efficacy was noted across devices and FFR types. gram-positive organisms were more difficult to completely eliminate than were gram-negative organisms. Prior to initiating FFR decontamination practices, institutions should verify the effectiveness of their devices and the safety of treated FFR.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".