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Record W4388857891 · doi:10.1155/2023/6641824

Quantifying Ventilation Design, Room Layout, and Occupant Activity Parameters during Aerosol-Generating Medical Procedures in Hospitals

2023· article· en· W4388857891 on OpenAlexaff
Cole Christianson, Jared B. Baylis, Vicki Komisar, Joshua Brinkerhoff

Bibliographic record

VenueIndoor Air · 2023
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsVentilation (architecture)OccupancyEmergency medicineAerosolMedicineEnvironmental scienceAirborne transmissionMedical emergencyOperations managementSimulationEngineeringCoronavirus disease 2019 (COVID-19)Architectural engineeringMeteorologyDiseaseMechanical engineering

Abstract

fetched live from OpenAlex

The risk of airborne disease transmission in hospital rooms during aerosol-generating medical procedures is known to be influenced by the size of the room, air ventilation rate, input-to-output flow ratio, vent surface area, and vent location. However, quantitative recommendations for each ventilation design parameter are scarce. Moreover, room layout and occupant activity parameters, such as furniture locations and healthcare worker movement, are often omitted from studies on airborne disease transmission in hospital settings. As a result, the development of policies and technologies aimed at mitigating airborne disease transmission in hospitals has been limited. To address this shortfall, this study is aimed at first characterizing existing ventilation, room layout, and occupancy parameters in hospital rooms where aerosol generation medical procedures (AGMPs) occur and then testing the hypotheses that ventilation, room layout, and occupancy parameters vary significantly between hospital rooms and, in some cases, with time. Information on AGMPs was collected via a survey circulated to healthcare workers within British Columbia’s Interior Health Authority (IHA), while hospital room and ventilation system information was collected by reviewing drawing packages of 37 IHA hospital rooms. The survey results indicate that AGMPs commonly occur in trauma, ICU, or general ward rooms with positive or negative pressure ventilation systems. Statistical tests, with room type (trauma, ICU, or general), room pressure (positive or negative), and/or time as independent variables, show that variables relating to ventilation (number of supply vents, supply and exhaust vent location, ventilation rate, and supply and exhaust area) and room layout (congestion score, room volume, light area, and number of lights) vary with room type but not with room pressure. Occupant activity variables (number of workers, number of moving workers, and speed score) also vary with room type, although to differing extent with room pressure and time. The survey and drawing review data presented in this study can help guide systematic comparisons of mitigative technologies as well as parametric investigations on how room layout, ventilation, and operational parameters influence airborne disease spread. This is a crucial first step in achieving quantitative and clinically relevant recommendations for mitigating airborne disease transmission in healthcare 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)0.001
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 score0.074
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.040
GPT teacher head0.316
Teacher spread0.276 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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