Validity and utility of urinary CXCL10/Cr immune monitoring in pediatric kidney transplant recipients
Bibliographic record
Abstract
Individualized posttransplant immunosuppression is hampered by suboptimal monitoring strategies. To validate the utility of urinary CXCL10/Cr immune monitoring in children, we conducted a multicenter prospective observational study in children <21 years with serial and biopsy-associated urine samples (n = 97). Biopsies (n = 240) were categorized as normal (NOR), rejection (>i1t1; REJ), indeterminate (IND), BKV infection, and leukocyturia (LEU). An independent pediatric cohort of 180 urines was used for external validation. Ninety-seven patients aged 11.4 ± 5.5 years showed elevated urinary CXCL10/Cr in REJ (3.1, IQR 1.1, 16.4; P < .001) and BKV nephropathy (median = 5.6, IQR 1.3, 26.9; P < .001) vs. NOR (0.8, IQR 0.4, 1.5). The AUC for REJ vs. NOR was 0.76 (95% CI 0.66-0.86). Low (0.63) and high (4.08) CXCL10/Cr levels defined high sensitivity and specificity thresholds, respectively; validated against an independent sample set (AUC = 0.76, 95% CI 0.66-0.86). Serial urines anticipated REJ up to 4 weeks prior to biopsy and declined within 1 month following treatment. Elevated mean CXCL10/Cr was correlated with first-year eGFR decline (ρ = -0.37, P ≤ .001), particularly when persistently exceeding ≥4.08 (ratio = 0.81; P < .04). Useful thresholds for urinary CXCL10/Cr levels reproducibly define the risk of rejection, immune quiescence, and decline in allograft function for use in real-time clinical monitoring in children.
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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 | 0.007 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".