Endoscopic Ultrasound-Guided Management of Pancreatic Fluid Collections: Update and Review of the Literature
Bibliographic record
Abstract
Severe acute pancreatitis is often complicated by the development of pancreatic fluid collections (PFCs), which may be associated with significant morbidity and mortality. It is crucial to accurately classify these collections as a pseudocyst or walled-off necrosis (WON) given significant differences in outcomes and management. Interventions for PFCs have increasingly shifted to less invasive strategies, with endoscopic ultrasound (EUS)-guided methods being shown to be safer and equally effective as more invasive surgical techniques. In recent years, many new developments have improved the safety and efficacy of EUS-guided interventions, such as the introduction of lumen-apposing metal stents (LAMS), direct endoscopic necrosectomy (DEN) and multiple other adjunctive techniques. Despite these developments, treatment of PFCs, and infected WON in particular, continues to be associated with significant morbidity and mortality. In this article, we discuss the EUS-guided management of PFCs while reviewing the latest developments and controversies in the field. We end by summarizing our own approach to managing PFCs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".