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Record W3028719402 · doi:10.1136/emermed-2020-209785

Endotracheal intubation with barrier protection

2020· article· en· W3028719402 on OpenAlexaff
Farah Jazuli, Monika Bilic, Erich Hanel, Michael Ha, Kelly Hassall, Brendon Trotter

Bibliographic record

VenueEmergency Medicine Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster University
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)AerosolSkin barrierIntubationMedical emergencySurgeryDermatologyPathologyInfectious disease (medical specialty)DiseaseMeteorology

Abstract

fetched live from OpenAlex

Given the high risk of healthcare worker (HCW) infection with COVID-19 during aerosol-generating medical procedures, the use of a box barrier during intubation for protection of HCWs has been examined. Previous simulation work has demonstrated its efficacy in protecting HCWs from cough-expelled droplets. Our objective was to assess its ability to protect HCWs against aerosols generated during aerosol-generating medical procedures. We used a battery-powered vapouriser to assess movement of vapour with: (1) no barrier; (2) a box barrier; and (3) a box barrier and a plastic sheet covering the box and patient's body. We visualised the trajectory of vapour and saw that the vapour remained within the barrier space when the box barrier and plastic sheet were used. This is in contrast to the box barrier alone, where vapour diffused towards the feet of the patient and throughout the room, and to no barrier where the vapour immediately diffused to the laryngoscopist. This demonstrates that the box with the plastic sheet has the potential to limit the spread of aerosols towards the laryngoscopist, and thus may play a role in protecting HCWs during aerosol-generating medical procedures. This is of particular importance in the care of patients with suspected COVID-19.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.614
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0390.000

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 head0.302
Teacher spread0.255 · 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.

Study designNot applicable
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

Citations16
Published2020
Admission routes1
Has abstractyes

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