Recipient Outcomes Following Transplantation of Allografts From Live Kidney Donors Who Subsequently Developed End-Stage Renal Disease
Bibliographic record
Abstract
Live kidney donors have an increased risk of end-stage renal disease (ESRD) compared with nondonors; however, it is unknown whether undetected, subclinical kidney disease exists at donation that subsequently contributes to this risk. To indirectly test this hypothesis, the authors followed the donated kidneys, by comparing the outcomes of 257 recipients whose donors subsequently developed ESRD with a matched cohort whose donors remained ESRD free. The compared recipients were matched on donor (age, sex, race/ethnicity, donor-recipient relationship), transplant (HLA mismatch, peak panel-reactive antibody, previous transplantation, year of transplantation), and recipient (age, sex, race/ethnicity, body mass index, cause of ESRD, and time on dialysis) risk factors. Median recipient follow-up was 12.5 years (interquartile range 7.4-17.9, maximum 20 years). Recipients of allografts from donors who developed ESRD had increased death-censored graft loss (74% versus 56% at 20 years; adjusted hazard ratio [aHR] 1.7; 95% confidence interval [CI] 1.5-2.0; p < 0.001) and mortality (61% versus 46% at 20 years; aHR 1.5; 95% CI 1.2-1.8; p < 0.001) compared with matched recipients of allografts from donors who did not develop ESRD. This association was similar among related, spousal, and unrelated nonspousal donors. These findings support a novel view of the mechanisms underlying donor ESRD: that of pre-donation kidney disease. However, biopsy data may be required to confirm this hypothesis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".