Impact of Acute Rejection and New-Onset Diabetes on Long-Term Transplant Graft and Patient Survival
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Development of new therapeutic strategies to improve long-term transplant outcomes requires improved understanding of the mechanisms by which these complications limit long-term transplant survival. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The association of acute rejection and new-onset diabetes was determined in the first posttransplantation year with the outcomes of transplant failure from any cause, death-censored graft loss, and death with a functioning graft in 27,707 adult recipients of first kidney-only transplants, with graft survival of at least 1 yr, performed between 1995 and 2002 in the United States. RESULTS: In multivariate analyses, patients who developed acute rejection or new-onset diabetes had a similar risk for transplant failure from any cause, but the mechanisms of transplant failure were different: Acute rejection was associated with death-censored graft loss but only weakly associated with death with a functioning graft. In contrast new-onset diabetes was not associated with death-censored graft loss but was associated with an increased risk for death with a functioning graft. CONCLUSIONS: Acute rejection and new-onset diabetes have a similar impact on long-term transplant survival but lead to transplant failure through different mechanisms. The mechanisms by which new-onset diabetes leads to transplant failure should be prospectively studied. Targeted therapeutic strategies to minimize the impact of various early posttransplantation complications may lead to improved long-term outcomes.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".