Technical Considerations and Confounders for Urine CXCL10 Chemokine Measurement
Bibliographic record
Abstract
Background. The urine C-X-C motif chemokine 10 (CXCL10) is a promising screening biomarker for renal allograft rejection. The aim of the study was to investigate important technical and biological aspects as well as potential confounders when measuring urine CXCL10. Methods. We analyzed 595 urine samples from 117 patients, who participated in a randomized controlled trial investigating the clinical utility of urine CXCL10 monitoring for posttransplant management. Urine CXCL10 was measured by an immunoassay using electrochemiluminescence. Results. Intraassay coefficient of variation was 2.5%, and interassay coefficient of variation was 10%. Urine CXCL10 remained stable (ie, <10% degradation) for 8 hours at 25°C or 37°C and for 3 days at 4°C. CXCL10 concentrations [pg/mL] strongly correlated with urine CXCL10/creatinine ratios [ng/mmol] (r2 = 0.98; P < 0.0001). Leucocyturia and active BK-polyomavirus infection are associated with higher CXCL10 concentrations, while allograft function, serum CRP, patient age, proteinuria, urine pH, hematuria, squamous epithelia cell count, and bacteriuria did not correlate with urine CXCL10 concentrations. In 145 paired samples obtained within 1–2 weeks, 80% showed a CXCL10/creatinine ratio change of < ±2 ng/mmol or ±50%, respectively. Conclusions. Urine CXCL10 measurement on the used platform is accurate and robust. Leucocyturia and active BK-polyomavirus infection are major confounders, which can be easily detected but represent important diagnostic “blind spots” when using urine CXCL10 to screen for allograft rejection. The intraindividual biological variability of urine CXCL10 within 1–2 weeks is mostly below ±50%, which is still much higher than the technical variability due to sample handling/processing (<20%).
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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 | 0.065 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".