Late Graft Loss After Kidney Transplantation: Is “Death With Function” Really Death With a Functioning Allograft?
Bibliographic record
Abstract
BACKGROUND: About half of late kidney allograft losses are attributed to death with function (DWF), a poorly characterized outcome. An ongoing question is whether DWF is a consequence of chronic allograft dysfunction. Using the prospective Long-term Deterioration of Kidney Allograft Function study database, we sought to better define the impact, phenotype, and clinical course of DWF in the current era. METHODS: Three thousand five hundred eighty-seven kidney recipients with functional grafts at 90 days post-transplant were followed prospectively for a median of 5.2 years. RESULTS: Characteristics at transplantation in those with DWF (N = 350, 9.8%) differed from those who otherwise lost their grafts (death-censored graft failure [DC-GF], N = 295, 8.2%) or maintained function (N = 2942, 82.0%); DWF patients were older, sicker, and had been on dialysis longer, with more preexisting cardiovascular disease, whereas DC-GF patients experienced more early rejection, more acute rejection after 90 days, and a clinically significant decrease in kidney function before graft failure. In contrast, the clinical course after transplantation in DWF patients did not differ before death from those who maintained function throughout. CONCLUSIONS: DWF and DC-GF in kidney transplant recipients represent differing clinical phenotypes occurring in distinct patient populations. Reducing the impact of DWF requires better definition of causes and clinical course and then trials of therapies to improve outcomes. Composite endpoints in clinical trials that group DWF and DC-GF together may obscure important clinical findings.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".