Patterns of End-Stage Renal Disease Caused by Diabetes, Hypertension, and Glomerulonephritis in Live Kidney Donors
Bibliographic record
Abstract
Inferences about late risk of end-stage renal disease (ESRD) in live kidney donors have been extrapolated from studies averaging <10 years of follow-up. Because early (<10 years) and late (≥10 years) postdonation ESRD may differ by causal mechanism, it is possible that extrapolations are misleading. To better understand postdonation ESRD, we studied patterns of common etiologies including diabetes, hypertension and glomerulonephritis (GN; as reported by providers) using donor registry data linked to ESRD registry data. Overall, 125 427 donors were observed for a median of 11.0 years (interquartile range 5.3–15.7 years; maximum 25 years). The cumulative incidence of ESRD increased from 10 events per 10 000 at 10 years after donation to 85 events per 10 000 at 25 years after donation (late vs. early ESRD, adjusted for age, race and sex: incidence rate ratio [IRR] 1.3 1.7 2.3 [subscripts are 95% confidence intervals]). Early postdonation ESRD was predominantly reported as GN-ESRD; however, late postdonation ESRD was more frequently reported as diabetic ESRD and hypertensive ESRD (IRR 2.3 7.7 25.2 and 1.4 2.6 4.6 , respectively). These time-dependent patterns were not seen with GN-ESRD (IRR 0.4 0.7 1.2 ). Because ESRD in live kidney donors has traditionally been reported in studies averaging <10 years of follow-up, our findings suggest caution in extrapolating such results over much longer intervals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".