Pancreas transplantation with grafts obtained from donation after cardiac death or donation after brain death results in comparable outcomes
Bibliographic record
Abstract
Introduction Pancreas organ shortages and long recipient waitlist times are critical components that limit recipients from receiving a pancreas transplant. Over the last decade, our center has been using donation after cardiac death (DCD) donors as an adjunct to donation after brain death (DBD) donors to expand the organ pool. The aim of this study was to compare recipient and graft survival between DCD and DBD recipients. Methods A retrospective single center propensity matched analysis (2011–2020) of 32 DCD vs 96 DBD pancreas transplants was performed. Results 8-year recipient survival was similar between DCD and DBD groups (87.4% vs 92.7%, p =0.35) as was simultaneous kidney and pancreas transplant (SPK) 8-year kidney (88.9 vs 96.9%, p =0.219) and pancreas graft survival (77.4% vs 86.7%, p =0.344). There was no difference in vascular thrombosis rate between DCD and DBD pancreas grafts (3.1% vs 7.3%, p =0.73). DCD kidneys had a higher rate of DGF vs DBD kidneys (28.1% vs 6.3%, p=0.004), without any significant difference in long term kidney failure (12.5% vs 8.3%, p =0.5). Discussion Recipients of DCD grafts demonstrate equivalent long-term patient and graft survival compared to DBD recipients for pancreas transplantation. Increased utilization of well selected DCD donors is a safe strategy to increase the donor pool.
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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 | 0.000 | 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) | 0.003 | 0.001 |
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".