Global Survey on Pancreatic Surgery During the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to clarify the role of pancreatic surgery during the COVID-19 pandemic to optimize patients' and clinicians' safety and safeguard health care capacity. SUMMARY BACKGROUND DATA: The COVID-19 pandemic heavily impacts health care systems worldwide. Cancer patients appear to have an increased risk for adverse events when infected by COVID-19, but the inability to receive oncological care seems may be an even larger threat, particularly in case of pancreatic cancer. METHODS: An online survey was submitted to all members of seven international pancreatic associations and study groups, investigating the impact of the COVID-19 pandemic on pancreatic surgery using 21 statements (April, 2020). Consensus was defined as >80% agreement among respondents and moderate agreement as 60% to 80% agreement. RESULTS: A total of 337 respondents from 267 centers and 37 countries spanning 5 continents completed the survey. Most respondents were surgeons (n = 302, 89.6%) and working in an academic center (n = 286, 84.9%). The majority of centers (n = 166, 62.2%) performed less pancreatic surgery because of the COVID-19 pandemic, reducing the weekly pancreatic resection rate from 3 [interquartile range (IQR) 2-5] to 1 (IQR 0-2) (P < 0.001). Most centers screened for COVID-19 before pancreatic surgery (n = 233, 87.3%). Consensus was reached on 13 statements and 5 statements achieved moderate agreement. CONCLUSIONS: This global survey elucidates the role of pancreatic surgery during the COVID-19 pandemic, regarding patient selection for the surgical and oncological treatment of pancreatic diseases to support clinical decision-making and creating a starting point for further discussion.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".