Bibliographic record
Abstract
PURPOSE OF REVIEW: This review provides an update on the surgical management of acute pancreatitis, with a focus on evidence accumulated over the past year regarding the optimal approach to pancreatic debridement in the critically ill patient. RECENT FINDINGS: Infected pancreatic necrosis remains the primary indication for surgery in patients with acute pancreatitis. Up to a quarter of patients with acute pancreatitis develop early bacteremia and pneumonia, and assessment of patients for surgery should include a thorough search for nonpancreatic sources of infection. Retroperitoneal, percutaneous and endoscopic approaches to pancreatic debridement can be used with success in appropriately selected critically ill patients. All minimally invasive approaches to necrosectomy are evolving, and there is currently insufficient evidence to advocate one approach over another. Management of patients with acute pancreatitis at high-volume centers appears to be associated with a survival benefit. SUMMARY: The existing evidence demonstrates that control of infected pancreatic necrosis without laparotomy is possible with appropriate patient selection. Evidence regarding minimally invasive approaches to pancreatic debridement remains of limited quality.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".