Risk Factors for Early Pancreatic Allograft Thrombosis Following Simultaneous Pancreas-Kidney Transplantation: A Systematic Review
Bibliographic record
Abstract
Simultaneous pancreas-kidney (SPK) transplantation remains the most effective treatment for providing consistent and long-term euglycemia in patients having type 1 diabetes with renal failure. Thrombosis of the pancreatic vasculature continues to contribute significantly to early graft failure and loss. We compared the rate of thrombosis to graft loss and systematically reviewed risk factors impacting early thrombosis of the pancreas allograft following SPK transplantation. We searched the MEDLINE, EMBASE, The Cochrane Library, and PREMEDLINE databases for studies reporting thrombosis following pancreas transplantation. Identified publications were screened for inclusion and synthesized into a data extraction sheet. Sixty-three studies satisfied eligibility criteria: 39 cohort studies, 22 conference abstracts, and 2 meta-analyses. Newcastle-Ottawa Scale appraisal of included studies demonstrated cohort studies of low bias risk; 1127 thrombi were identified in 15 936 deceased donor, whole pancreas transplants, conferring a 7.07% overall thrombosis rate. Thrombosis resulted in pancreatic allograft loss in 83.3% of reported cases. This review has established significant associations between donor and recipient characteristics, procurement and preservation methodology, transplantation technique, postoperative management, and increased risk of early thrombosis in the pancreas allograft. Further studies examining the type of organ preservation fluid, prophylactic heparin protocol, and exocrine drainage method and early thrombosis should also be performed.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".