Staff perceptions of military chemical–biological–radiological–nuclear (CBRN) air-purifying masks during a simulated clinical task in the context of SARS-CoV-2
Bibliographic record
Abstract
Air-purifying full-face masks, such as military chemical–biological–radiological–nuclear masks, might offer superior protection against severe acute respiratory syndrome coronavirus 2 compared to disposable polypropylene P2 or N95 masks. In addition, disposable masks are in short supply, while military chemical–biological–radiological–nuclear masks can be disinfected then reused. It is unknown whether such masks might be appropriate for civilians with minimal training in their use. Accordingly, we compared the Australian Defence Force in-service chemical–biological–radiological–nuclear Low Burden Mask (AirBoss Defense, Newmarket, Canada) with polypropylene N95 masks and non-occlusive glasses worn during simulated tasks performed by civilian clinicians in an Australian tertiary referral hospital intensive care unit. After brief training in the use of the Low Burden Mask, participants undertook a simulated cardiac arrest scenario. Previous training with polypropylene N95 masks had been provided. Evaluation of 10 characteristics of each mask type were recorded, and time to mask application was assessed. Thirty-three participants tested the Low Burden Mask, and 28 evaluated polypropylene N95 masks and glasses. The Low Burden Mask was donned more quickly: mean time 7.0 (standard deviation 2.1) versus 18.3 (standard deviation 6.7) seconds; P = 0.0076. The Low Burden Mask was rated significantly higher in eight of the 10 assessed criteria, including ease of donning, comfort (initially and over a prolonged period), fogging, seal, safety while removing, confidence in protection, and overall. Visibility and communication ability were rated equally highly for both systems. We conclude that this air-purifying full-face mask is acceptable to clinicians in a civilian intensive care unit. It enhances staff confidence, reduces waste, and is likely to be a lower logistical burden during a prolonged pandemic. Formal testing of effectiveness is warranted.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".