Pre-operative testing and personal protective equipment in the operating room during a pandemic: A survey of Ontario general surgeons
Bibliographic record
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic has had major implications for general surgery practice. We sought to characterize general surgeons' perceptions of their surgical practice in Ontario, Canada, regarding operating room precautions to maximize safety during the pandemic. Methods: A web-administered cross-sectional survey was sent to general surgeons registered with the College of Physicians and Surgeons of Ontario on May 19, 2020. Surgeons were surveyed regarding their practices in pre-operative severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing, use of intra-operative personal protective equipment (PPE) given a patient's COVID-19 status, and management of common general surgical emergencies with COVID-19 patients. Responses were compared between surgeons from high- and low-prevalence public health units (PHUs) in Ontario using chi-square tests. Results: There were 81 respondents (rate: 81/271, 30%), 48 (59%) of whom were from a PHU in the top quartile of COVID-19 prevalence. Surgeons from low-prevalence PHUs reported pre-procedural COVID-19 testing rates similar to those reported in high-prevalence PHUs for elective (36% versus 55%), urgent (36% versus 54%), and emergent (20% versus 33%) surgeries. Seventy-eight percent of surgeons with COVID-19-negative patients limited trainees in the operating room compared with 96% of surgeons with COVID-19-positive patients. Use of N95 respirators was 17% for surgeons with COVID-19-negative patients, which dramatically increased to 62% for surgeons with patients whose COVID-19 status was unknown. Conclusions: These findings support a need for improved understanding of local disease prevalence and risk of COVID-19 transmission to conserve PPE and return surgical trainees to pre-pandemic standards.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".