Evidence-Based Strategies for the Treatment of Peritoneal Malignancies during Health Care Resource Restriction: The COVID-19 Pandemic
Bibliographic record
Abstract
Background: The COVID-19 pandemic has put enormous pressure on hospital resources, and has affected all aspects of patient care. As operative volumes decrease, cancer surgeries must be triaged and prioritized with careful thought and attention to ensure maximal benefit for the maximum number of patients. Peritoneal malignancies present a unique challenge, as surgical management can be resource intensive, but patients have limited non-surgical treatment options. This review summarizes current data on outcomes and resource utilization to help inform decision-making and case prioritization in times of constrained health care resources. Methods: A rapid literature review was performed, examining surgical and non-surgical outcomes data for peritoneal malignancies. Narrative data synthesis was cross-referenced with relevant societal guidelines. Peritoneal malignancy surgeons and medical oncologists reviewed recommendations to establish a national perspective on case triage and mitigating treatment strategies. Results and Conclusions: Triage of peritoneal malignancies during this time of restricted health care resource is nuanced and requires multidisciplinary discussion with consideration of individual patient factors. Prioritization should be given to patients where delay may compromise resectability of disease, and where alternative treatment options are lacking. Mitigating strategies such as systemic chemotherapy and/or surgical deferral may be utilized with close surveillance for disease stability or progression, which may affect surgical urgency. Unique hospital capacity, and ability to manage the complex post-operative course for these patients must also be considered to ensure patient and system needs are aligned.
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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 | 0.023 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".