Aerosolization in Endoscopic Sinus Surgery and Risk Mitigation in the COVID-19 Era: A Scoping Review
Bibliographic record
Abstract
Abstract Objective The continually evolving coronavirus disease 2019 (COVID-19) pandemic has created a dire need for rapid reorganization of health care delivery within surgical services. Ensuing initial reports of high infection rates following endoscopic sinus and skull base surgery, various expert and societal guidelines have emerged. We hereby provide a scoping review of the available literature on endoscopic sinus and skull base surgery, exploring both the risk of aerosolization and expert recommendations on surgical management during the pandemic. Methods A literature search of the PubMed database was performed up until May 9th, 2020. Additionally, websites and published statements from otolaryngology associations were searched for recommendations. This scoping review followed the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta Analyses Extension for Scoping Reviews. Results A total of 29 peer-reviewed publications and statements from expert recommendations or professional associations were included. Current expert guidance relies mainly on scarce, anecdotal evidence, and two cadaveric studies, which have demonstrated potential aerosolization during transnasal surgery. General consensus exists for delaying surgery when possible, ascertaining COVID-19 status preoperatively and donning of adequate personal protective equipment by all operating room staff (including at minimum an N95 mask). Cold, nonpowered surgical instruments are deemed the safest, while thermal instruments (electrocautery and laser) and high-speed drills should be minimized. Conflicting recommendations emerge for use of microdebriders. Conclusion Endoscopic sinus and skull base surgery impart a potential risk of aerosolization. Hence, surgical indications, protective measures for health care workers, and surgical instrumentation must be adapted accordingly in the COVID-19 context.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".