Pancreatic Anastomosis in Robotic-Assisted Pancreaticoduodenectomy: Different Surgical Techniques
Bibliographic record
Abstract
Robot-assisted pancreatoduodenectomy (R-PD) may provide challenges but potential benefits for pancreatic-enteric anastomosis fashioning. Despite numerous trials comparing different pancreatic-enteric anastomosis techniques, an ideal method is still missing. This study aims to describe different management strategies and surgical techniques of standardized pancreatic-enteric anastomoses during an R-PD. This study reported the robotic technical steps of the modified end-to-side Blumgart pancreaticojejunostomy, the Cattel-Warren duct-to-mucosa pancreatojejunostomy, with internal or external pancreatic duct stent, and the modified end-to-side, double-layer pancreogastrostomy. A dual-console da Vinci Xi Surgical System® (Intuitive Surgical Xi, Sunnyvale, CA) was used to perform all the R-PD. Different robotic pancreatic-enteric anastomosis techniques can be used during the reconstruction phase, possibly reproducing the open technique. The type of anastomosis and applied mitigation strategies should balance surgical strategy adaptability and operative technique standardization. R-PD should be performed in high-volume centers by surgeons with extensive experience in pancreatic and advanced MI surgery, enabling different but standardized anastomotic techniques based on patients' risk factors and intraoperative findings. Future studies on robotic pancreatic anastomosis should focus on personalized approaches after adequate risk stratification.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".