Multicenter Validation of Urinary CXCL9 as a Risk-Stratifying Biomarker for Kidney Transplant Injury
Bibliographic record
Abstract
Noninvasive biomarkers are needed to assess immune risk and ultimately guide therapeutic decision-making following kidney transplantation. A requisite step toward these goals is validation of markers that diagnose and/or predict relevant transplant endpoints. The Clinical Trials in Organ Transplantation-01 protocol is a multicenter observational study of biomarkers in 280 adult and pediatric first kidney transplant recipients. We compared and validated urinary mRNAs and proteins as biomarkers to diagnose biopsy-proven acute rejection (AR) and stratify patients into groups based on risk for developing AR or progressive renal dysfunction. Among markers tested for diagnosing AR, urinary CXCL9 mRNA (odds ratio [OR] 2.77, positive predictive value [PPV] 61.5%, negative predictive value [NPV] 83%) and CXCL9 protein (OR 3.40, PPV 67.6%, NPV 92%) were the most robust. Low urinary CXCL9 protein in 6-month posttransplant urines obtained from stable allograft recipients classified individuals least likely to develop future AR or a decrement in estimated glomerular filtration rate between 6 and 24 months (92.5-99.3% NPV). Our results support using urinary CXCL9 for clinical decision-making following kidney transplantation. In the context of acute dysfunction, low values can rule out infectious/immunological causes of injury. Absent urinary CXCL9 at 6 months posttransplant defines a subgroup at low risk for incipient immune injury.
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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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".