Impact of Early Graft Function on 10-Year Graft Survival in Recipients of Kidneys From Standard- or Expanded-Criteria Donors
Bibliographic record
Abstract
BACKGROUND: The use of kidneys from expanded-criteria donors (ECD) is regarded with caution. METHODS: We compared 279 kidney transplant recipients (KTxR) from standard-criteria donors (SCD) and 237 from ECD, transplanted between January 1990 and December 2006. We evaluated the impact of immediate graft function (IGF), slow graft function (SGF), and delayed graft function (DGF) and the drop in estimated glomerular filtration rate (ΔeGFR) ≤ 30% or > 30% during the first year after transplantation on long-term patient and death-censored graft survival (DCGS). RESULTS: Ten-year patient survival was similar in SCD- or ECD-KTxR (P = 0.38). DCGS was better in SCD-KTxR versus ECD-KTxR (77.3% vs. 67.3%; P = 0.01). DCGS did not differ in either group experiencing IGF (P = 0.17) or DGF (P = 0.12). However, DCGS was worse in ECD-KTxR experiencing SGF (84.9% vs. 73.7%; P = 0.04). Predictors of DCGS were 1-year serum creatinine (hazard ratio, 1.03; P < 0.0001) and ΔeGFR > 30% between 1 and 12 months (Δ1-12eGFR) after transplantation (hazard ratio, 2.2; P = 0.02). In ECD-KTxR with IGF and more than 1-year follow-up, 10-year DCGS was better in those with Δ1-12eGFR ≤ 30% versus those with Δ1-12eGFR > 30% (83.8% vs. 53.6%; P = 0.01). CONCLUSION: Recipients of SCD or ECD kidneys with IGF or DGF had similar 10-year patient survival and DCGS. SGF had a worse impact on DCGS in ECD-KTxR. In addition to 1-year serum creatinine, Δ1-12eGFR > 30% is a negative predictor of DCGS. Larger studies should confirm if increasing the use of ECD, avoiding factors that contribute to SGF or DGF, and/or a decline in eGFR during the first year after transplantation may expand the donor pool and result in acceptable 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 | 0.002 | 0.003 |
| 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 | 0.000 |
| 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".