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Record W4308866887 · doi:10.3390/ma15227918

Prolonged Use of Surgical Masks and Respirators Affects the Protection and Comfort for Healthcare Workers

2022· article· en· W4308866887 on OpenAlexafffund
Xiaoning Guan, Jing Lin, Jia-Xiang Han, Xiaodong Gao, Ying Zhang, Bijie Hu, Robert Guidoin, Lu Wang

Bibliographic record

VenueMaterials · 2022
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsUniversité Laval
FundersFundamental Research Funds for the Central UniversitiesHigher Education Discipline Innovation ProjectDonghua UniversityScience and Technology Commission of Shanghai MunicipalityUniversité Laval
KeywordsRespiratorFace masksMedicineCoronavirus disease 2019 (COVID-19)Materials scienceInternal medicineComposite material

Abstract

fetched live from OpenAlex

This study explored the ideal period for wearing masks to prevent the physiological and psychological problems associated with long-term face mask use during respiratory infections by healthcare workers. Breathing simulators, surgical masks (SM) and medical respirators (PM) were prepared for two to eight hours. Changes in the comfort of masks (facial skin temperature, breathing resistance, and moisture permeability) and protection (filtration efficiency, resistance to blood penetration, and colony count) were assessed. The results demonstrated that the masks offered efficient liquid-particle filtering even after eight hours of use. However, the number of bacterial colonies using PM and SM grew significantly after two and four hours, respectively. Concerning comfort, the inspiratory resistance of masks rose dramatically after two hours, whereas the moisture permeability declined considerably after four hours. In addition, skin temperature had a significant increase within two hours, which may result in facial discomfort. When conditions permitted, the hospital staff was instructed to replace their masks every two hours.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.131

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.051
GPT teacher head0.295
Teacher spread0.244 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations21
Published2022
Admission routes2
Has abstractyes

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