Duodenal leaks after pancreas transplantation with enteric drainage - characteristics and risk factors
Bibliographic record
Abstract
Pancreas-kidney transplantation with enteric drainage has become a standard treatment in diabetic patients with renal failure. Leaks of the graft duodenum (DL) remain a significant complication after transplantation. We studied incidence and predisposing factors of DLs in both simultaneous pancreas-kidney (SPK) and pancreas after kidney (PAK) transplantation. Between January 2002 and April 2013, 284 pancreas transplantations were performed including 191 SPK (67.3%) and 93 PAK (32.7%). Patient data were analyzed for occurrence of DLs, risk factors, leak etiology, and graft survival. Of 18 DLs (incidence 6.3%), 12 (67%) occurred within the first 100 days after transplantation. Six grafts (33%) were rescued by duodenal segment resection. Risk factors for a DL were PAK transplantation sequence (odds ratio 3.526, P = 0.008) and preoperative immunosuppression (odds ratio 3.328, P = 0.012). In the SPK subgroup, postoperative peak amylase as marker of preservation/reperfusion injury and recipient pretransplantation cardiovascular interventions as marker of atherosclerosis severity were associated with an increased incidence of DLs. CMV-mismatch constellations showed an increased incidence in the SPK subgroup, however without significance probability. Long-term immunosuppression in PAK transplantation is a major risk factor for DLs. Early surgical revision offers the chance of graft rescue.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".