Impact of Surgical Technique on Reducing the Incidence of Postoperative Pancreatic Fistula: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Postoperative pancreatic fistula (POPF), a complication frequently encountered following pancreaticoduodenectomy. Pancreaticojejunostomy (PJ) and pancreaticogastrostomy (PG) are the most frequently utilized reconstructive strategies with different anastomosis techniques. This study was conducted to evaluate the optimal surgical technique to prevent POPF. Methods: The study was conducted using PRISMA guidelines with PROSPERO registration No. CRD42023494393. Patients undergoing pancreaticoduodenectomy includes the use of PJ or PG with different anastomoses techniques and developed POPF were included. We conducted a systematic literature review from January 2019 to December 2023 using a comprehensive search strategy, through Web of Science, ProQuest, Science Direct, PubMed, and Google Scholar databases. Meta-analysis was utilized to analyze the outcomes. The risk of bias was assessed using the Newcastle-Ottawa scale. Results: Eighteen studies with 3343 patients who underwent various anastomoses (including the Modified DuVal, Heidelberg PJ, and Blumgart methods) were included. Postoperative pancreatic fistula (POPF) occurred in 27% of patients. Techniques such as modified Heidelberg, Peng, shark mouth PJ, and Kiguchi PJ were associated with lower POPF rates, whereas modified and classical Blumgart techniques exhibited higher rates. While mortality rates varied among surgical techniques, overall mortality was low. Conclusions: Data from this study can be used to shape future studies and direct physicians to develop strategies to reduce the risk of POPF and thereby reduce morbidity and mortality, leading to improved patient outcomes. Furthermore, this data can inform clinical decision-making and guide the development of evidence-based practice guidelines to optimize surgical outcomes.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".