Pancreatico-Jejunostomy Fistula After Pancreaticoduodenectomy: Where Do We Stand? Results from an International Survey
Bibliographic record
Abstract
INTRODUCTION: Pancreatico-duodenectomy (PD) remains one of the most complex abdominal surgeries, and pancreatico-jejunostomy (PJ) fistula is its most critical postoperative complication. In efforts to reduce the incidence of postoperative pancreatic fistula (POPF), several PJ techniques and adjuncts, including stents, have been recommended. This article presents data from an international survey regarding PJ methods, the use of pancreatic stents, and their correlation with POPF rates from surgical centers worldwide. METHODS: -value < 0.05 was considered to be statistically significant. RESULTS: A total of 122 units of pancreatic surgery from 26 countries distributed across five continents responded to the survey. Most centers performed less than 50 PDs a year, preferred a duct-to-mucosa PJ, and employed a stent routinely. Mean POPF grade B and C incidences were lower in high-volume (15.24% ± 7.29 and 3.95% ± 2.39) and in PJ stent-using centers (16.25% ± 8.7 and 5.37% ± 7.49). CONCLUSIONS: Institutional case volume and stent usage are more crucial determinants of POPF incidence than the PJ technique itself. Centralization and standardization of PD procedures are related to reductions in major fistula rates.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".