Urinary biomarkers of renal transplant outcome
Bibliographic record
Abstract
PURPOSE OF REVIEW: Renal allograft loss remains an important cause of morbidity and mortality. The objective of this review was to provide a rationale for noninvasive monitoring to identify patients at high risk for graft loss; discuss key steps in prognostic biomarker development from bench-to-bedside; and review promising biomarkers for late renal allograft outcomes. RECENT FINDINGS: In a multicentre prospective cohort, early 6-month urinary CCL2 was demonstrated to be associated with the development of 24-month interstitial fibrosis/tubular atrophy and inflammation (IFTA+i). These findings were extended to a single centre cohort, which showed that 6-month urinary CCL2 was a predictor of death-censored graft loss independent of donor-specific antibody and delayed graft function. In a large, multicentre prospective observational study (CTOT-01), 6-month urinary CXCL9 was significantly associated with more than 30% decline of graft function at 24 months. SUMMARY: Urinary chemokines may identify recipients who are at high risk of graft loss. The early detection of high-risk recipients may allow for more intensive posttransplant surveillance; avoidance of drug minimization/withdrawal protocols; and the identification of patients who may benefit from enrolment in novel interventional trials. Prospective trials are needed to demonstrate that urinary chemokine-guided posttransplant surveillance strategies improve long-term graft 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".