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Record W3133699794 · doi:10.1097/ccm.0000000000004965

Transmission of Severe Acute Respiratory Syndrome Coronavirus 1 and Severe Acute Respiratory Syndrome Coronavirus 2 During Aerosol-Generating Procedures in Critical Care: A Systematic Review and Meta-Analysis of Observational Studies*

2021· review· en· W3133699794 on OpenAlexaff
Vinson Wai‐Shun Chan, Helen Ng, Laiba Rahman, Audrey Tang, Kwan Pui Tang, Alex Mok, Jeremy Ho Pak Liu, Kenny Shiu Cheong Ho, Shannon M. Chan, Sunny H. Wong, Anthony Yuen Bun Teoh, Albert Kam Ming Chan, Martin C. S. Wong, Yuhong Yuan, Jeremy Yuen‐Chun Teoh

Bibliographic record

VenueCritical Care Medicine · 2021
Typereview
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineOdds ratioMeta-analysisIntensive careMiddle East respiratory syndrome coronavirusCoronavirusInternal medicineIntensive care medicineCoronavirus disease 2019 (COVID-19)Disease

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess the risk of coronavirus transmission to healthcare workers performing aerosol-generating procedures and the potential benefits of personal protective equipment during these procedures. DATA SOURCES: MEDLINE, EMBASE, and Cochrane CENTRAL were searched using a combination of related MeSH terms and keywords. STUDY SELECTION: Cohort studies and case controls investigating common anesthetic and critical care aerosol-generating procedures and transmission of severe acute respiratory syndrome coronavirus 1, Middle East respiratory syndrome coronavirus, and severe acute respiratory syndrome coronavirus 2 to healthcare workers were included for quantitative analysis. DATA EXTRACTION: Qualitative and quantitative data on the transmission of severe acute respiratory syndrome coronavirus 1, severe acute respiratory syndrome coronavirus 2, and Middle East respiratory syndrome coronavirus to healthcare workers via aerosol-generating procedures in anesthesia and critical care were collected independently. The Risk Of Bias In Non-randomized Studies - of Interventions tool was used to assess the risk of bias of included studies. DATA SYNTHESIS: Seventeen studies out of 2,676 yielded records were included for meta-analyses. Endotracheal intubation (odds ratio, 6.69, 95% CI, 3.81-11.72; p < 0.001), noninvasive ventilation (odds ratio, 3.65; 95% CI, 1.86-7.19; p < 0.001), and administration of nebulized medications (odds ratio, 10.03; 95% CI, 1.98-50.69; p = 0.005) were found to increase the odds of healthcare workers contracting severe acute respiratory syndrome coronavirus 1 or severe acute respiratory syndrome coronavirus 2. The use of N95 masks (odds ratio, 0.11; 95% CI, 0.03-0.39; p < 0.001), gowns (odds ratio, 0.59; 95% CI, 0.48-0.73; p < 0.001), and gloves (odds ratio, 0.39; 95% CI, 0.29-0.53; p < 0.001) were found to be significantly protective of healthcare workers from contracting severe acute respiratory syndrome coronavirus 1 or severe acute respiratory syndrome coronavirus 2. CONCLUSIONS: Specific aerosol-generating procedures are high risk for the transmission of severe acute respiratory syndrome coronavirus 1 and severe acute respiratory syndrome coronavirus 2 from patients to healthcare workers. Personal protective equipment reduce the odds of contracting severe acute respiratory syndrome coronavirus 1 and severe acute respiratory syndrome coronavirus 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.068
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0210.041
Bibliometrics0.0090.011
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.227
GPT teacher head0.456
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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

Citations48
Published2021
Admission routes1
Has abstractyes

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