Robotic Lateral Pancreaticojejunostomy for Chronic Pancreatitis
Bibliographic record
Abstract
Lateral pancreaticojejunostomy (LPJ) has shown good postoperative outcomes in patients with painful, morphine dependent, chronic pancreatitis (CP). The recent rise of robotic and laparoscopic pancreatic surgery has found benefits such as reduced time to functional recovery. Few studies have reported on the feasibility, technique and outcome of robotic LPJ, especially including transection of the gastroduodenal artery. The present study describes the main steps for robotic LPJ in a patient with painful chronic pancreatitis with a dilated main pancreatic duct. The patient underwent robotic LPJ. The LPJ anastomosis is performed using a running suture technique in a longitudinal side-to-side manner. Routinely, the gastroduodenal artery is transected to drain the entire length of the main pancreatic duct. The patient is in French position; 7 trocars are placed (4 robotic, 2 laparoscopic assistants, 1 liver-retractor). After docking of the robot system, the omental bursa is opened, and the right gastroepiploic artery and vein are ligated at their base at the lower border of the pancreas. Intraoperative ultrasonography is performed in order to determine trajectory of the dilated main pancreatic duct which is opened for its entire length after the gastroduodenal artery has been suture ligated both cranially and caudally from the main pancreatic duct. A Roux limb is created, and a latero-lateral PJ is fashioned using several 3-0 barbed sutures. A stapled jejuno-jejunostomy is created at sufficient distance from the pancreatic anastomosis, aided by a 50 cm suture. The described technique for robotic LPJ is a complex but feasible operation for patients with treatment refractory CP and a dilated main pancreatic duct. Due to its complexity, implementation in high volume centers with extensive experience with CP surgery may improve 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".