Outcomes of Dual Adult Kidney Transplants in the United States: An Analysis of the OPTN/UNOS Database
Bibliographic record
Abstract
BACKGROUND: The organ shortage has resulted in increased use of kidneys from expanded criteria donors (ECD). For ECD kidneys unsuitable for single use, dual kidney transplants (DKT) may be possible. There are limited data comparing outcomes of DKT to single kidney ECD transplants, making it unclear where DKT fits in the current allocation scheme. Our purpose was to compare outcomes of DKT and ECD transplants in the United States. METHODS: From 2000 to 2005, a total of 625 DKT, 7686 single kidney ECD, and 6,044 SCD transplants from donors aged>or=50 years were identified from the Organ Procurement and Transplantation Network/United Network for Organ Sharing data. Allograft survival was the primary outcome. RESULTS: DKT comprised 4% of kidney transplants from donors aged>or=50 years. Compared to the ECD donor group, the DKT donor group was older (mean age 64.6+/-7.7 years vs. 59.9+/-6.2 years) and consisted of more African Americans (13.1% vs. 9.9%), and more diabetic donors (16.3% vs. 10.4%; P<0.001). Mean cold ischemic time was longer in DKT (22.2+/-9.7 hr), but rates of delayed graft function were lower (29.3%) compared to ECD transplants (33.6%, P=0.03). Three-year overall graft survival was 79.8% for DKT and 78.3% for ECD transplants. CONCLUSION: DKT were infrequent and had outcomes comparable to ECD transplants, despite the use of organs from higher risk donors. With a more upfront approach to DKT by offering this option to patients at the time of wait-listing as part of an ECD algorithm, we may be able to further optimize outcomes of DKT and minimize discard of potential organs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".