Late Allograft Loss and Contemporary Cardiorenal Metabolic Therapies
Bibliographic record
Abstract
Late kidney allograft loss occurs through one of two mechanisms: ( 1 ) deterioration of kidney function leading to retransplantation or dialysis (death-censored graft loss) and ( 2 ) premature death with a normally functioning transplant (death with graft function)-each accounting for approximately 50% of late kidney graft losses. Late death-censored graft loss typically results from a combination of immune and nonimmune events leading to common nonspecific end points ( e.g ., tubular atrophy, interstitial fibrosis, and glomerulosclerosis). Conversely, leading causes of death with graft function typically include cardiovascular events, malignancy, and infection. With an improved understanding of the multiple mechanism by which late graft dysfunction develops, there is an opportunity to identify patients at greatest risk and institute novel strategies to quell the process. Newer cardiometabolic agents with proven benefit in the general population have not been well-studied in kidney transplant recipients. However, in addition to their potential benefits in reducing cardiovascular, infectious, and malignancy end points (thus minimizing death with graft function risk), many novel agents may have additional anti-inflammatory and/or antifibrotic benefit (minimizing death-censored graft loss risk) in the kidney transplant population. In this review, we summarize existing literature regarding major causes of death-censored graft loss and death with graft function and discuss the potential roles of new cardiorenal metabolic agents including sodium-glucose cotransport 2 inhibitors, nonsteroidal mineralocorticoid receptor antagonists, glucagon-like peptide 1 receptor agonists, and dual endothelin and angiotensin receptor antagonists in the kidney transplant population, including potential mechanisms to improve death with graft function and death-censored graft loss 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".