Associations between Deceased-Donor Urine Injury Biomarkers and Kidney Transplant Outcomes
Bibliographic record
Abstract
Assessment of deceased-donor organ quality is integral to transplant allocation practices, but tools to more precisely measure donor kidney injury and better predict outcomes are needed. In this study, we assessed associations between injury biomarkers in deceased-donor urine and the following outcomes: donor AKI (stage 2 or greater), recipient delayed graft function (defined as dialysis in first week post-transplant), and recipient 6-month eGFR. We measured urinary concentrations of microalbumin, neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), IL-18, and liver-type fatty acid binding protein (L-FABP) from 1304 deceased donors at organ procurement, among whom 112 (9%) had AKI. Each biomarker strongly associated with AKI in adjusted analyses. Among 2441 kidney transplant recipients, 31% experienced delayed graft function, and mean±SD 6-month eGFR was 55.7±23.5 ml/min per 1.73 m(2) In analyses adjusted for donor and recipient characteristics, higher donor urinary NGAL concentrations associated with recipient delayed graft function (highest versus lowest NGAL tertile relative risk, 1.21; 95% confidence interval, 1.02 to 1.43). Linear regression analyses of 6-month recipient renal function demonstrated that higher urinary NGAL and L-FABP concentrations associated with slightly lower 6-month eGFR only among recipients without delayed graft function. In summary, donor urine injury biomarkers strongly associate with donor AKI but provide limited value in predicting delayed graft function or early allograft function after transplant.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".