Urinary biomarkers of renal transplant outcome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it