Shipping living donor kidneys and transplant recipient outcomes
Bibliographic record
Abstract
Kidney paired donation (KPD) is an important tool to facilitate living donor kidney transplantation (LDKT). Concerns remain over prolonged cold ischemia times (CIT) associated with shipping kidneys long distances through KPD. We examined the association between CIT and delayed graft function (DGF), allograft survival, and patient survival for 1267 shipped and 205 nonshipped/internal KPD LDKTs facilitated by the National Kidney Registry in the United States from 2008 to 2015, compared to 4800 unrelated, nonshipped, non-KPD LDKTs. Shipped KPD recipients had a median CIT of 9.3 hours (range = 0.25-23.9 hours), compared to 1.0 hour for internal KPD transplants and 0.93 hours for non-KPD LDKTs. Each hour of CIT was associated with a 5% increased odds of DGF (adjusted odds ratio: 1.05, 95% confidence interval [CI], 1.02-1.09, P < .01). However, there was not a significant association between CIT and all-cause graft failure (adjusted hazard ratio [aHR]: 1.01, 95% CI: 0.98-1.04, P = .4), death-censored graft failure ( [aHR]: 1.02, 95% CI, 0.98-1.06, P = .4), or mortality (aHR 1.00, 95% CI, 0.96-1.04, P > .9). This study of KPD-facilitated LDKTs found no evidence that long CIT is a concern for reduced graft or patient survival. Studies with longer follow-up are needed to refine our understanding of the safety of shipping donor kidneys through KPD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".