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Record W3046694405 · doi:10.1111/anae.15227

Measurement of airborne particle exposure during simulated tracheal intubation using various proposed aerosol containment devices during the COVID‐19 pandemic

2020· letter· en· W3046694405 on OpenAlexafffund
Ana Sjaus, Marguerite d’Entremont

Bibliographic record

VenueAnaesthesia · 2020
Typeletter
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsDalhousie University
FundersCanadian Anesthesiologists' SocietyCanadian Anesthesia Research Foundation
KeywordsAerosolCoronavirus disease 2019 (COVID-19)MedicineSuctionRespiratorParticle (ecology)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Intubation2019-20 coronavirus outbreakAirborne transmissionEnvironmental scienceIntensive care medicineMeteorologySurgeryDiseasePathologyPhysicsChemistry

Abstract

fetched live from OpenAlex

We read with great interest the article by Simpson et al. [1]. We are grateful to the authors for providing the first scientific evaluation on the impact of improvised barrier ‘devices’ on dispersion of exhaled aerosols. As the authors point out, with the exception of the sealed box with suction, all were found to cause no significant decrease in ambient aerosol particles. In the case of the ‘aerosol box’, rather worryingly, increases in particle counts were recorded. These findings are highly concerning given the seemingly widespread use of such devices during aerosol-generating procedures in patients with COVID-19. As we reconcile the duty to care for patients in the face of this highly transmissible and potentially deadly disease, the requirement for healthcare worker protection is an issue of respecting basic human rights as much as their psychological need to reduce anxiety in a highly stressful environment. However human and understandable the fear may be, we cannot forget that our work is first based on science. We can, and should, turn to science for potential solutions. Based on their findings, we heartily support the decision by Simpson et al. to remove passive barriers from their intubation protocols. We do, however, feel compelled to comment on several aspects of the study. Excluding the emitted aerosols from ventilation in the room will result in a highly concentrated plume. This will favour aerosol escape via apertures on the rigid box at times of sudden increases in internal pressure. This phenomenon in the Simpson et al. experiments was likely facilitated by the conditions of negative pressure room ventilation [1]. Here, it becomes necessary to think about what is happening outside the box; airflow in the space around the box becomes crucial to understanding the pathways of dispersion and areas of the greatest exposure. The authors positioned the particle counter based on the presumed relevance to laryngoscopists’ exposure and recorded the changes in aerosol counts. However, there may have been nearby locations with even higher counts, including those relevant to the assistant, as was alluded to in the manuscript. Conversely, a particle counter located in another position might not have picked up any spikes at all. To avoid trial and error in selecting optimal sampling locations, computational fluid dynamic modelling simulation of the particular room with the appropriately set boundary conditions can be performed [2]. A relatively minor change in position of the particle counter with respect to the room airflow patterns could lead to a significant degree of variation in aerosol dispersion and particle counts. Controlling for the variability of airflow, in addition to humidity and room temperature, may be difficult but is necessary to achieve results that truly reflect the effect of the barrier. We wonder how different the results would be if the experiments were done in a ‘positive pressure room’, a type of ventilation that is present in most operating theatres. In addition, the position of the laryngoscopist (and the point of origin and direction of flow of their exhaled breath) is dictated by the ergonomic properties of each device. Given that participants wore simple procedure masks, unaccounted for variations in airflow and possible droplet contamination due to their breathing were likely introduced. The finding that the five micron particles were more likely to be sampled outside the aerosol box and the sealed box was puzzling. At the high end of the size spectrum for aerosols, these relatively large particles are likely to settle rapidly. Depending on the exact conditions during baseline measurements, the location of sampling and possible environmental contamination may have contributed to this finding. Lastly, the most interesting result from the problem-solving perspective is related to the performance of the sealed box with suction. By virtue of its construction, the box precluded airway management but performed remarkably well in reducing aerosol egress. To find solutions that truly improve safety, we need sound engineering solutions that are acceptable to users and patients and validated through rigorous testing protocols rooted in scientific principles. These solutions take time, expertise and multidisciplinary collaboration, the Simpson et al. article being an excellent example. Our front-line healthcare workers and patients deserve nothing less.

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)0.000
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 score0.110
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.275
Teacher spread0.228 · 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

Citations13
Published2020
Admission routes2
Has abstractyes

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