Strategies for the Delay of Surgery in the Management of Resectable Hepatobiliary Malignancies during the COVID-19 Pandemic
Bibliographic record
Abstract
Objective: We aimed to review data about delaying strategies for the management of hepatobiliary cancers requiring surgery during the covid-19 pandemic. Background: Given the covid-19 pandemic, many jurisdictions, to spare resources, have limited access to operating rooms for elective surgical activity, including cancer, thus forcing deferral or cancellation of cancer surgeries. Surgery for hepatobiliary cancer is high-risk and particularly resource-intensive. Surgeons must critically appraise which patients will benefit most from surgery and which ones have other therapeutic options to delay surgery. Little guidance is currently available about potential delaying strategies for hepatobiliary cancers when surgery is not possible. Methods: An international multidisciplinary panel reviewed the available literature to summarize data relating to standard-of-care surgical management and possible mitigating strategies to be used as a bridge to surgery for colorectal liver metastases, hepatocellular carcinoma, gallbladder cancer, intrahepatic cholangiocarcinoma, and hilar cholangiocarcinoma. Results: Outcomes of surgery during the covid-19 pandemic are reviewed. Resource requirements are summarized, including logistics and adverse effects profiles for hepatectomy and delaying strategies using systemic, percutaneous and radiation ablative, and liver embolic therapies. For each cancer type, the long-term oncologic outcomes of hepatectomy and the clinical tools that can be used to prognosticate for individual patients are detailed. Conclusions: There are a variety of delaying strategies to consider if availability of operating rooms decreases. This review summarizes available data to provide guidance about possible delaying strategies depending on patient, resource, institution, and systems factors. Multidisciplinary team discussions should be leveraged to consider patient- and tumour-specific information for each individual case.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".