Recipients of Kidneys from Expanded Criteria Donors Whose eGFR Does not Drop >30% Between 1-12 Months after Transplantation Have Excellent Long-Term Graft Survival
Bibliographic record
Abstract
Purpose: To determine the impact of eGFR drop lower or greater than 30% during the first yr post-kidney transplantation (KTx) on long-term death censored graft survival (DCGS). Methods: We studied 471 KTx recipients of deceased donor kidneys between 1/1990 and 12/2006. 253 pts (53.7%, 82 women, 171 men, 49±12 yrs) received standard criteria donor (SCD) kidneys, and 218 pts (46.3%, 73 women, 145 men, 52±13 yrs) received ECD kidneys. Immunosuppression consisted of ATG induction, CNI and an antimetabolite. CIT was 15.4±8.4 hr. We analyzed an eGFR drop lower or greater than 30% between 1-3, 1-12 and 3-12 months in recipients of SCD and ECD kidneys, with immediate (IGF, Scr decreased ≥20% within 24 hrs post-KTx), slow (SGF, Scr decreased < 20% within 24 hrs post-KTx and no need for dialysis) or delayed graft function (DGF, need for dialysis during the first week post-KTx), on long-term DCGS in pts whose graft survived >1 yr post-KTx. 55 recipients of SCD and 34 recipients of ECD were excluded because of graft loss, death or loss to follow-up during the first yr. Results: The impact of eGFR drop on long-term DCGS is depicted in Figures 1 and 2. There was no difference in pts with SGF. eGFR (mL/min/1.73m2) at 1, 5 and 10 yrs was 71±22, 66±22 and 57±22 respectively, in recipients of SCD and 56±18, 49±23 and 41±20 respectively, in recipients of ECD (P=0.001). An eGFR drop between 1-12 months was associated with lower DCGS (HR 2.16, P=0.02).Figure: [*P=0.002 and **P=0.01 vs. ECD Drop >30%]Figure: [P<0.0001 and **P=0.0003 vs. ECD Drop >30%]Conclusion: Recipients of ECD kidneys without an eGFR drop >30% between 1-12 months post-KTx have excellent long-term DCGS, equivalent to recipients of SCD kidneys.
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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 | 0.001 |
| 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 | 0.000 | 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".