The view from 10,000 procedures: technical tips and wisdom from master pancreatic surgeons to avoid hemorrhage during pancreaticoduodenectomy
Bibliographic record
Abstract
Pancreaticoduodenectomy remains the exclusive technique for surgical resection of cancers located within both the pancreatic head and periampullary region. Amongst peri-procedural complications, hemorrhage is particularly problematic given that allogenic blood transfusions are known to increase the risk of infection, acute lung injury, cancer recurrence and overall 30-day morbidity and mortality rates. Because blood loss can be considered a modifiable factor that reflects surgical technique, rates of perioperative blood loss and transfusion have been advocated as robust quality indicators. We present a correspondence manuscript that outlines peri-procedural concepts detailing a successful pancreaticoduodenectomy with minimal hemorrhage. These tips were collated from master pancreatic surgeons throughout the globe who have performed over 10,000 cumulative pancreaticoduodenectomies. At risk scenarios for hemorrhage include dissections of the superior mesenteric - portal vein, gastroduodenal artery, and retroperitoneal soft tissue margin. General principles in limiting slow continuous hemorrhage that may accumulate into larger total case losses are also discussed. While many of the techniques and tips proposed by master pancreas surgeons are intuitive and straight forward, when taken as a collective they represent a significant contribution to improved outcomes associated with the pancreaticoduodenectomy over the past 100 years.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.014 | 0.029 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".