The Prognostic Implications of Renal Function Recovery and Delayed Graft Function in Kidney Transplantation
Bibliographic record
Abstract
Introduction In the general population, lack of renal function recovery (RFR), 90 days after acute kidney injury (AKI) is a contributor to inferior renal outcomes and mortality. Ischemia and reperfusion during kidney transplantation (KT) contributes to AKI and delayed graft function (DGF), a severe form of AKI. Our aim was to analyze the long-term prognostic implications of RFR at 90 days, in recipients with and without DGF. Methods This was a retrospective analysis of adult deceased donor KT recipients (1996 -2016). Outcome of interest was death censored graft failure (DCGF). RFR was calculated using the formula (observed eGFR/predicted eGFR) X 100. Predicted eGFR was half of the donor eGFR plus pre-emptive recipient eGFR, and observed eGFR was the average of 3 best values, 90 days post-KT. Grafts with primary non-function were excluded. The Chronic Kidney Disease Epidemiology Collaboration prediction equation was used to determine the eGFR. Results 941 KT recipients were eligible for analysis, of which 25% had DGF. RFR was divided into 3 tertiles <75% (360), 75-100% (218) and >100% (363). Higher DCGF was noted in recipients that developed DGF, and had RFR<75% (Figure 1). In recipients that developed DGF, RFR had no further prognostic implications on DCGF (Table 1). However, in those that did not develop DGF, RFR<75% was associated with an 82% and 89% higher DCGF in univariate and multivariate analysis, respectively.Conclusion In recipients that do not develop DGF, 90-day RFR<75% was associated with DCGF. This indicates the prognostic relevance of RFR as a short-term outcome as most KT do not develop DGF. We propose that to truly capture the impact of AKI and ischemia and reperfusion injury during the transplantation surgery, one must quantify RFR over 90 days after KT.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".