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Record W3025748074 · doi:10.1101/2020.05.14.20094821

Transmission of aerosols through pristine and reprocessed N95 respirators

2020· preprint· en· W3025748074 on OpenAlexafffund
Paul Chen, Aldrich Ngan, Niclas Manson, Jason T. Maynes, Gregory H. Borschel, Ori D. Rotstein, Frank Gu

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsSt. Michael's HospitalHospital for Sick ChildrenUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsRespiratorLimitingCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Personal protective equipmentEnvironmental scienceMaterials scienceMedicineInfectious disease (medical specialty)EngineeringComposite materialDiseaseMechanical engineering

Abstract

fetched live from OpenAlex

Abstract During the Covid-19 pandemic, pristine and reprocessed N95 respirators are crucial equipment towards limiting nosocomial infections. The NIOSH test certifying the N95 rating, however, poorly simulates aerosols in healthcare settings, limiting our understanding of the exposure risk for healthcare workers wearing these masks, especially reprocessed ones. We used experimental conditions that simulated the sizes, densities and airflow properties of infectious aerosols in healthcare settings. We analyzed the penetration and leakage of aerosols through pristine and reprocessed N95 respirators. Seven reprocessing methods were investigated. Our findings suggest that pristine and properly reprocessed N95 respirators effectively limit exposure to infectious aerosols, but that care must be taken to avoid the elucidated degradation mechanisms and limit noncompliant wear.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.043
GPT teacher head0.311
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2020
Admission routes2
Has abstractyes

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