MétaCan
Menu
Back to cohort
Record W3026893066 · doi:10.1503/cjs.007020

Operating during COVID-19: Is there a risk of viral transmission from surgical smoke during surgery?

2020· letter· en· W3026893066 on OpenAlexaffvenue
Phil Vourtzoumis, Nawar A. Alkhamesi, Ahmad Elnahas, Jeffrey E. Hawel, Christopher M. Schlachta

Bibliographic record

VenueCanadian Journal of Surgery · 2020
Typeletter
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakTransmission (telecommunications)SmokePandemicBetacoronavirusCoronavirus InfectionsSurgeryVirologyInternal medicineInfectious disease (medical specialty)OutbreakWaste management

Abstract

fetched live from OpenAlex

Summary: The World Health Organization declared a pandemic when coronavirus disease 2019 (COVID-19) started to sweep the globe. Growing concerns for the safety of health care workers was raised when up to 80% of people with COVID-19 showed mild or no symptoms at all. Some surgical procedures will be inevitable during the pandemic, and proper safety measures must be in place to avoid transmission risks. Surgical smoke is a common by-product from the use of energy devices in the operating room. The effects of surgical smoke have been studied for more than 40 years, and potential health hazards have been reported. Chemicals, carcinogens and biologically active materials, such as bacteria and viruses, have been isolated in surgical smoke. To ensure the safety of operating room personnel, we must consider whether there is any concern of viral transmission from the inhalation of surgical smoke.

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)0.001
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.074
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0220.016
Insufficient payload (model declined to judge)0.0070.003

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 head0.087
GPT teacher head0.320
Teacher spread0.233 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations22
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of SurgerySame topicCOVID-19 and healthcare impactsFrench-language works237,207