Minimizing the Risk of Aerosol Contamination During Elective Lung Resection Surgery
Bibliographic record
Abstract
BACKGROUND: In the setting of the COVID-19 pandemic, the conduct of elective cancer surgery has become an issue because of the need to balance the requirement to treat patients with the possibility of transmission of the virus by asymptomatic carriers. A particular concern is the potential for viral transmission by way of aerosol which may be generated during perioperative care. There are currently no guidelines for the conduct of elective lung resection surgery in this context. METHODS: A working group composed of 1 thoracic surgeon, 2 anesthesiologists and 1 critical care specialist assessed the risk for aerosol during lung resection surgery and proposed steps for mitigation. After external review, a final draft was approved by the Committee for the Governance of Perioperative and Surgical Activities of the Hôpital Maisonneuve-Rosemont, in Montreal, Canada. RESULTS: The working group divided the risk for aerosol into 6 time-points: (1) intubation and extubation; (2) Lung isolation and patient positioning; (3) access to the chest; (4) conduct of the surgical procedure; (5) procedure termination and lung re-expansion; (6) chest drainage. Mitigating strategies were proposed for each time-point. CONCLUSIONS: The situation with COVID-19 is an opportunity to re-evaluate operating room protocols both for the purposes of this pandemic and similar situations in the future. In the context of lung resection surgery, specific time points during the procedure seem to pose specific risks for the genesis of aerosol and thus should be the focus of attention.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".