Hepatobiliary anastomotic leakage: a narrative review of definitions, grading systems, and consequences of leaks
Bibliographic record
Abstract
Background and Objective: Hepatobiliary diseases are a longstanding and significant medical challenge which, despite advances in surgical techniques, still carry risks for postoperative complications such as anastomotic leaks (ALs), which can include both postoperative pancreatic fistula (POPF) and bile leaks (BL). These complications incur significant human and economic costs on all those involved, including the patient, healthcare providers, and hospital systems. The aim of this study was to construct a narrative review of literature surrounding definitions and grading systems for ALs in the context of hepato-pancreato-biliary (HPB) procedures, and consequences of POPF and BL. Methods: A literature review was conducted by examining databases including PubMed, Web of Science, OVID Embase, Google Scholar, and Cochrane library databases. Searches were performed with the following search criteria: (((((((anastomosis) OR (anastomotic leak*)) OR (postoperative pancreatic fistula)) OR (bile leak*)) OR (pancreaticoduodenectomy)) OR (whipple)) AND ((hepatobiliary) OR (hepato-pancreato-biliary)) AND ((definition) OR (grading system*) OR (consequences) OR (outcomes) OR (risk factor*) OR (morbidity) OR (mortality))). Publications that were retrieved underwent further assessment to ensure other relevant publications were identified and included. Key Content and Findings: A universally accepted definition and grading system for POPF and BL continues to be lacking, leading to variability in reported incidence in the literature. Various groups have worked to publish guidelines for defining and grading POPF and BL, with the International Study Group in Pancreatic Surgery (ISGPS) and International Study Group for Liver Surgery (ISGLS) definitions the current most recommended definitions for POPF and BL, respectively. The burden of AL on patients, healthcare providers, and hospitals is well documented in evidence from leak consequences, such as increased morbidity and mortality, higher reoperation rates, and increased readmission rates, among others. Conclusions: AL remains a significant challenge in HPB surgery, despite medical advancements. Understanding the progress made in defining and grading leaks, as well as the range of negative outcomes that arise from AL, is crucial in improving patient care, reduce surgical mortality, and drive further advancements in earlier detection and treatment of AL.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".