International Delphi Expert Consensus on Safe Return to Surgical and Endoscopic Practice: From the Coronavirus Global Surgical Collaborative.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.306 | 0.259 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".