Early renal function recovery and long-term graft survival in kidney transplantation
Bibliographic record
Abstract
Following kidney transplantation (KTx), renal function improves gradually until a baseline eGFR is achieved. Whether or not a recipient achieves the best-predicted eGFR after KTx may have important implications for immediate patient management, as well as for long-term graft survival. The aim of this cohort study was to calculate the renal function recovery (RFR) based on recipient and donor eGFR and to evaluate the association between RFR and long-term death-censored graft failure (DCGF). We studied 790 KTx recipients between January 1990 and August 2014. The last donor SCr prior to organ procurement was used to estimate donor GFR. Recipient eGFR was calculated using the average of the best three SCr values observed during the first 3 months post-KTx. RFR was defined as the ratio of recipient eGFR to half the donor eGFR. 53% of recipients had an RFR ≥1. There were 127 death-censored graft failures (16%). Recipients with an RFR ≥1 had less DCGF compared with those with an RFR <1 (HR 0.56; 95% CI 0.37-0.85; P = 0.006). Transplant era, acute rejection, ECD and DGF were also significant determinants of graft failure. Early recovery of predicted eGFR based on donor eGFR is associated with less DCGF after KTx.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".