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Record W3170400370 · doi:10.1093/milmed/usab225

Effectiveness of Selected Air Cleaning Devices During Dental Procedures

2021· article· en· W3170400370 on OpenAlexaff
T Maurais, J Kriese, Magali Fournier, Laurence Langevin, B MacLeod, S Blier, J P Tessier-Guay, Abigail L Girardin, L Maheux

Bibliographic record

VenueMilitary Medicine · 2021
Typearticle
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsCanadian Armed Forces
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)MedicineDental EquipmentAerosolDentistryDental clinicPersonal protective equipmentSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicMedical emergencyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

INTRODUCTION: The recent COVID-19 pandemic has underscored the necessity of protecting health care providers (HCPs) against the transmission of infectious agents during dental procedures. To this end, the effectiveness of several air cleaning devices (ACDs) in reducing HCPs exposure to aerosols generated during dental procedures was estimated, separately or in combination with each other. These ACDs were a chairside unit capturing aerosols at the source of generation, and four ambient ACDs: a portable ambient ACD; a negative pressure module; a custom made, fan-operated and wall-mounted air filter (WMAF); and a smaller and passive version of the latter. The last three ACDs were intended for mobile dental clinics (MDCs) only. MATERIALS AND METHODS: This assessment was performed in two different environments: in a dental clinic operatory and in a MDC. Two dental personnel, acting in the roles of dentist and dental assistant, performed on simulated patient aerosol-generating and non-aerosol-generating procedures. For each 5-minute scenario, the cumulative exposure to airborne particulate matter 10 µm in size or smaller (PM10) was determined by calculating the sum of all 1 second readings obtained with personal and ambient air monitors. The effectiveness of the ACDs in capturing PM10 was estimated based on the capability of the ACDs to keep PM10 level at or below the initial background level. RESULTS: In all conditions assessed in the dental clinic operatory, when both the chairside and portable ambient ACDs were functioning, an estimated effectiveness of 100% in capturing PM10 was achieved. In the MDC, in all conditions where the chairside ACD was used without the negative pressure module, an estimated effectiveness of 100% was also achieved. The simultaneous operation of the negative pressure module in the MDC, which led to a room negative pressure of -0.25 inch wc, reduced the chairside ACD's effectiveness in capturing aerosols. Conversely, the use of the WMAF in the MDC in combination with the chairside ACD further reduced exposure to PM10 below the initial background level. Nonetheless, in all conditions assessed in both settings (dental clinic operatory and MDC), larger visible aerosols were produced, often landing on the surrounding environment. A fair portion of these aerosols landed on the inside of the chairside ACD flange. CONCLUSIONS: This assessment suggests that the use of the tested chairside ACD, by capturing aerosols at the source of generation, had the greatest impact on reducing exposure of dental personnel to PM10 produced during dental procedures. This study also indicates that such exposure is further reduced with the addition of an ambient ACD. However, creating a negative pressure room as high as -0.25 inch wc can lead to air turbulence reducing the effectiveness of ACDs in capturing aerosols at the source. Furthermore, the presence of uncaptured droplets and spatter on the surrounding environment supports the need to complement the use of engineering controls with proper administrative controls and personal protective equipment, as recommended by governmental agencies and the scientific community for preventing the transmission of infection in health care settings.

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)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.313
Teacher spread0.300 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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