Current Evidence for Minimally Invasive Surgery During the COVID-19 Pandemic and Risk Mitigation Strategies
Bibliographic record
Abstract
OBJECTIVE: Our objective was to review the literature surrounding the risks of viral transmission during laparoscopic surgery and propose mitigation measures to address these risks. SUMMARY BACKGROUND DATA: The SARS-CoV-2 pandemic has caused surgeons the world over to re-evaluate their approach to surgical procedures given concerns over the risk of aerosolization of viral particles and exposure of operating room staff to infection. International society guidelines advise against the use of laparoscopy; however, the evidence on this topic is scant and recommendations are based on the perceived most cautious course of action. METHODS: We conducted a narrative review of the existing literature surrounding the risks of viral transmission during laparoscopic surgery and balance these risks against the benefits of minimally invasive approaches. We also propose mitigation measures to address these risks that we have adopted in our institution. RESULTS AND CONCLUSION: While it is currently assumed that open surgery minimizes operating room staff exposure to the virus, our findings reveal that this may not be the case. A well-informed, evidence-based opinion is critical when making decisions regarding which operative approach to pursue, for the safety and well-being of the patient, the operating room staff, and the healthcare system at large. Minimally invasive surgical approaches offer significant advantages with respect to both patient care, and the mitigation of the risk of viral transmission during surgery, provided the appropriate equipment and expertise are present.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".