International Delphi Expert Consensus on Safe Return to Surgical and Endoscopic Practice
Bibliographic record
Abstract
Objective: The aim of this work is to formulate recommendations based on global expert consensus to guide the surgical community on the safe resumption of surgical and endoscopic activities. Background: The COVID-19 pandemic has caused marked disruptions in the delivery of surgical care worldwide. A thoughtful, structured approach to resuming surgical services is necessary as the impact of COVID-19 becomes better controlled. The Coronavirus Global Surgical Collaborative sought to formulate, through rigorous scientific methodology, consensus-based recommendations in collaboration with a multidisciplinary group of international experts and policymakers. Methods: Recommendations were developed following a Delphi process. Domain topics were formulated and subsequently subdivided into questions pertinent to different aspects of surgical care in the COVID-19 crisis. Forty-four experts from 15 countries across 4 continents drafted statements based on the specific questions. Anonymous Delphi voting on the statements was performed in 2 rounds, as well as in a telepresence meeting. Results: One hundred statements were formulated across 10 domains. The statements addressed terminology, impact on procedural services, patient/staff safety, managing a backlog of surgeries, methods to restart and sustain surgical services, education, and research. Eighty-three of the statements were approved during the first round of Delphi voting, and 11 during the second round. A final telepresence meeting and discussion yielded acceptance of 5 other statements. Conclusions: The Delphi process resulted in 99 recommendations. These consensus statements provide expert guidance, based on scientific methodology, for the safe resumption of surgical activities during the COVID-19 pandemic.
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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 | 0.346 | 0.276 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".