Evolution of renal function and urinary biomarker indicators of inflammation on serial kidney biopsies in pediatric kidney transplant recipients with and without rejection
Bibliographic record
Abstract
Urinary CXCL10 and metabolites are biomarkers independently associated with TCMR. We sought to test whether these biomarkers fluctuate in association with histological severity of TCMR over short time frames. Forty-nine pairs of renal biopsies obtained 1-3 months apart from 40 pediatric renal transplant recipients were each scored for TCMR acuity score (i + t; Banff criteria). Urinary CXCL10:Cr and TCMR MDS were obtained at each biopsy and were tested for association with changes between biopsies in acuity, estimated GFR (ΔeGFR), and 12-month ΔeGFR. Sequential biopsies were obtained 1.8 ± 0.8 months apart. Biopsy 1 was usually obtained under protocol (75%), and 62% percent had evidence of TCMR. Using each biopsy pair for comparison, ΔeGFR did not predict change in acuity. By contrast, change in acuity was significantly correlated with change in urinary CXCL10:Cr (ρ 0.45, P = .003) and MDS (ρ 0.29, P = .04) between biopsies. The 12-month ΔeGFR was not predicted by TCMR acuity or CXCL10:Cr at Biopsy 2; however, an inverse correlation was seen with urinary MDS (ρ -0.35; P = .02). Changes in eGFR correlate poorly with evolving TCMR acuity on histology. Urinary biomarkers may be superior for non-invasive monitoring of rejection, including histological response to therapy, and may be prognostic for medium-term function.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".