Preserving the Pancreas Graft: Outcomes of Surgical Repair of Duodenal Leaks in Enterically Drained Pancreas Allografts
Bibliographic record
Abstract
BACKGROUND: Duodenal leak remains a major cause of morbidity and graft loss in pancreas transplant recipients. The role and efficacy of surgical and image-guided interventions to salvage enterically drained grafts with a duodenal leak has yet to be defined. METHODS: We investigated the incidence, treatment, and outcome of duodenal leak in 426 pancreas transplantation recipients from 2000 to 2015. RESULTS: Duodenal leak developed in 33 (7.8%) recipients after a median follow-up of 5.3 (range, 0.5-15.2) years. Most leaks occurred during the first year (n = 22; 67%), and most were located near the proximal and distal duodenal staple line. Graft pancreatectomy was performed in 8 patients as primary therapy because of unfavorable local and/or systemic conditions. Salvage was attempted in 25 patients using percutaneous drainage (n = 4), surgical drainage (n = 4), or surgical repair (n = 17). Percutaneous or surgical drainage failed to control the leak in 7 of these 8 patients, and all 7 ultimately required graft pancreatectomy for persistent leak and sepsis. Surgical repair salvaged 14 grafts, and 13 grafts continue to function after a median follow-up of 2.9 (range, 1.1-6.3) years after repair. CONCLUSIONS: Our study shows that in selected patients a duodenal leak can be repaired successfully and safely in enterically drained grafts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".