The Influence of Immediate Outcomes on Long-Term Results in Recipients of Kidney Transplants from Standard Criteria Versus Expanded Criteria Donors
Bibliographic record
Abstract
Purpose: To determine the impact of immediate outcomes on long-term patient and graft survival in recipients of expanded criteria donors (ECD) vs. standard criteria donor (SCD) kidneys. Methods: We studied 471 kidney transplants (KTx) recipients of deceased donors between 1/1990 and 12/2006. 253 pts (53.7%, 82 women, 171 men, 4912 yrs) received SCD kidneys, and 218 pts (46.3%, 73 women, 145 men, 5213 yrs) received ECD kidneys. Immunosuppression included ATG induction, CNI and an antimetabolite. We performed multivariate analyses including: Immediate graft function (IGF, Scr decreased >20% within 24 hrs post-KTx), slow graft function (SGF, Scr decreased < 20% within 24 hrs post-KTx and no need for dialysis) and delayed graft function (DGF, need for dialysis during the first week post-KTx); Tx era (before and after 7/1997); other conventional donor and recipient variables; ECD criteria (before and after 2002); and eGFR drop lower than or greater than 30% between 1-3, 1-12 and 3-12 months. Results: Patient and death censored graft survival are shown in Figures 1 and 2. Significant predictors of patient survival were: age (HR: 1.058, P< 0.0001), Scr at 1 yr (HR: 1.008, P< 0.0001), redo-KTx (HR: 2.134, P=0.0003), and use of tacrolimus at 1 month post-KTx (HR: 0.605, P=0.02). Significant predictors of death censored graft survival were: Scr at 1 yr (HR: 1.027, P< 0.0001) and eGFR drop >30% (1-12 months) (HR: 2.165, P=0.02).Figure: [P=NS for all comparisons]Figure: [ECD_SGF vs. SCD_SGF (P=0.04)]Conclusion: Recipients of KTx from SCD or ECD with IGF or DGF have similar long-term patient and death censored graft survival. In addition to known predictors, eGFR drop >30% (1-12 months post-KTx) is a strong negative predictor of death censored graft survival.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".