The Benefits of Preemptive Transplantation Using High–Kidney Donor Profile Index Kidneys
Bibliographic record
Abstract
BACKGROUND: The Kidney Donor Profile Index (KDPI) is a percentile score summarizing the likelihood of allograft failure: A KDPI ≥85% is associated with shorter allograft survival, and 50% of these donated kidneys are not currently used for transplantation. Preemptive transplantation (transplantation without prior maintenance dialysis) is associated with longer allograft survival than transplantation after dialysis; however, it is unknown whether this benefit extends to high-KDPI transplants. The objective of this analysis was to determine whether the benefit of preemptive transplantation extends to recipients of transplants with a KDPI ≥85%. METHODS: This retrospective cohort study compared the post-transplant outcomes of preemptive and nonpreemptive deceased donor kidney transplants using data from the Scientific Registry of Transplant Recipients. 120,091 patients who received their first, kidney-only transplant between January 1, 2005, and December 31, 2017, were studied, including 23,211 with KDPI ≥85%. Of this cohort, 12,331 patients received a transplant preemptively. Time-to-event models for the outcomes of allograft loss from any cause, death-censored graft loss, and death with a functioning transplant were performed. RESULTS: Compared with recipients of nonpreemptive transplants with a KDPI of 0%-20% as the reference group, the risk of allograft loss from any cause in recipients of a preemptive transplant with KDPI ≥85% (hazard ratio [HR], 1.51; 95% confidence interval [CI], 1.39 to 1.64) was lower than that in recipients of nonpreemptive transplant with a KDPI ≥85% (HR, 2.39; 95% CI, 2.21 to 2.58) and similar to that of recipients of a nonpreemptive transplant with a KDPI of 51%-84% (HR, 1.61; 95% CI, 1.52 to 1.70). CONCLUSIONS: Preemptive transplantation is associated with a lower risk of allograft failure, irrespective of KDPI, and preemptive transplants with KDPI ≥85% have comparable outcomes with nonpreemptive transplants with KDPI 51%-84%.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".