Outcomes From Pancreatic Transplantation in Donation After Cardiac Death
Bibliographic record
Abstract
BACKGROUND: Pancreas transplantation remains the gold standard for treatment for type I diabetes providing an insulin-independent, normoglycemic state. Increasingly, donation after cardiac death (DCD) donors are used in view of the organ donor shortage. We aimed to systematically review recipient outcomes from DCD donors and where possible compared these with donor after brain death (DBD) donors. METHODS: We searched the databases MEDLINE via PubMed, EMBASE, and The Cochrane Library from inception to March 2015, for studies reporting the outcome of DCD pancreas transplants. We appraised studies using the Newcastle-Ottawa scale and meta-analyzed using a random effects model. RESULTS: We identified 18 studies, 4 retrospective and 6 prospective cohort studies and 8 case reports. Our bias assessment revealed that although studies were well conducted, some studies had potential confounding factors and absence of comparator groups. Eight of the 18 studies included a DBD comparison group comprising 23 609 transplant recipients. Importantly, there was no significant difference in allograft survival up to 10 years (hazard ratio, 0.98; 95% confidence interval [95% CI], 0.74-1.31; P = 0.92), or patient survival (hazard ratio, 1.31; 95% CI, 0.62-2.78; P = 0.47) between DCD and DBD pancreas transplants. We estimated that the odds of graft thrombosis was 1.67 times higher in DCD organs (95% CI, 1.04-2.67; P = 0.006). However, subgroup analysis found thrombosis was not higher in recipients whose DCD donors were given antemortem heparin (P = 0.62). CONCLUSIONS: Using current DCD criteria, pancreas transplantation is a viable alternative to DBD transplantation, and antemortem interventions including heparinization may be beneficial. This potential benefit of DCD pancreas donation warrants further study.
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".