MétaCan
Menu
Back to cohort
Record W3127270105 · doi:10.1164/rccm.202008-3070oc

Quantitative Assessment of Viral Dispersion Associated with Respiratory Support Devices in a Simulated Critical Care Environment

2021· article· en· W3127270105 on OpenAlexaff
Hamed Avari, Ryan Hiebert, Agnes Ryzynski, Ariela Levy, Julie Nardi, Hasina Kanji-Jaffer, Peter Kiiza, Ruxandra Pinto, Simon Plenderleith, Robert Fowler, Hamza Mbareche, Samira Mubareka

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook HospitalSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineVentilation (architecture)NoseAerosolizationMechanical ventilationRespiratory systemAirborne transmissionAnesthesiaInhalationCoronavirus disease 2019 (COVID-19)SurgeryInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Abstract Rationale Patients with severe coronavirus disease (COVID-19) require supplemental oxygen and ventilatory support. It is unclear whether some respiratory support devices may increase the dispersion of infectious bioaerosols and thereby place healthcare workers at increased risk of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Objectives To quantitatively compare viral dispersion from invasive and noninvasive respiratory support modalities. Methods This study used a simulated ICU room with a breathing-patient simulator exhaling nebulized bacteriophages from the lower respiratory tract with various respiratory support modalities: invasive ventilation (through an endotracheal tube with an inflated cuff connected to a mechanical ventilator), helmet ventilation with a positive end-expiratory pressure (PEEP) valve, noninvasive bilevel positive-pressure ventilation, nonrebreather face masks, high-flow nasal oxygen (HFNO), and nasal prongs. Measurements and Main Results Invasive ventilation and helmet ventilation with a PEEP valve were associated with the lowest bacteriophage concentrations in the air, and HFNO and nasal prongs were associated with the highest concentrations. At the intubating position, bacteriophage concentrations associated with HFNO (2.66 × 104 plaque-forming units [PFU]/L of air sampled), nasal prongs (1.60 × 104 PFU/L of air sampled), nonrebreather face masks (7.87 × 102 PFU/L of air sampled), and bilevel positive airway pressure (1.91 × 102 PFU/L of air sampled) were significantly higher than those associated with invasive ventilation (P < 0.05 for each). The difference between bacteriophage concentrations associated with helmet ventilation with a PEEP valve (4.29 × 10–1 PFU/L of air sampled) and bacteriophage concentrations associated with invasive ventilation was not statistically significant. Conclusions These findings highlight the potential differential risk of dispersing virus among respiratory support devices and the importance of appropriate infection prevention and control practices and personal protective equipment for healthcare workers when caring for patients with transmissible respiratory viral infections such as SARS-CoV-2.

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)0.001
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 score0.105
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.023
GPT teacher head0.359
Teacher spread0.337 · 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

Citations28
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Respiratory and Critical Care MedicineSame topicInfection Control and VentilationFrench-language works237,207