Late graft failure after kidney transplantation as the consequence of late versus early events
Bibliographic record
Abstract
Beyond the first posttransplant year, 3% of kidney transplants fail annually. In a prospective, multicenter cohort study, we tested the relative impact of early versus late events on risk of long-term death-censored graft failure (DCGF). In grafts surviving at least 90 days, early events (acute rejection [AR] and delayed graft function [DGF] before day 90) were recorded; serum creatinine (Cr) at day 90 was defined as baseline. Thereafter, a 25% rise in serum Cr or new-onset proteinuria triggered graft biopsy (index biopsy, IBx), allowing comparison of risk of DCGF associated with early events (AR, DGF, baseline serum Cr >2.0 mg/dL) to that associated with later events (IBx). Among 3678 patients followed for 4.7 ± 1.9 years, 753 (20%) had IBx at a median of 15.3 months posttransplant. Early AR (HR = 1.77, P < .001) and elevated Cr at Day 90 (HR = 2.56, P < .0001) were associated with increased risk of DCGF; however, later-onset dysfunction requiring IBx had far greater impact (HR = 13.8, P < .0001). At 90 days, neither clinical characteristics nor early events distinguished those who subsequently did or did not undergo IBx or suffer DCGF. To improve long-term kidney allograft survival, management paradigms should promote prompt diagnosis and treatment of both early and later events.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".