Prediction of Long-term Renal Allograft Outcome By Early Urinary CXCL10 Chemokine Levels
Bibliographic record
Abstract
UNLABELLED: Predictive biomarkers for long-term renal allograft outcome could help to individualize follow-up strategies and therapeutic interventions. METHODS: We investigated the predictive value of urinary CXC chemokine ligand 10 (CXCL10) measured at different timepoints (ie, at 3 and 6 months, and mean of 3 and 6 months coined CXCL10-burden) for long-term allograft outcomes in 154 patients. The primary outcome was a composite graft endpoint of death-censored allograft loss and/or biopsy-proven rejection and/or decline of estimated glomerular filtration rate greater than 20% occurring beyond 6 months after transplantation. RESULTS: After a median follow-up of 6.6 years (interquartile range, 5.7-7.5 years) the endpoint was reached in 43/154 patients (28%). In a multivariable Cox-regression model independent predictors were 6-month CXCL10 levels, the CXCL10-burden, HLA-mismatches, donor age and delayed graft function while previous (sub)clinical rejection, estimated glomerular filtration rate and proteinuria at 6 months, as well as 3-month CXCL10 levels were not. Time-dependent receiver operating characteristic analysis revealed an area under the curve of 0.68 (6-month CXCL10) and 0.67 (CXCL10-burden). Grouped by optimal cutoff, low 6-month CXCL10 (<0.70 ng/mmol) was associated with a 95% endpoint-free 5-year survival compared to 78% with high 6-month CXCL10 (P = 0.0007). Only 2 of 62 patients (3%) with low 6-month CXCL10 levels (<0.70 ng/mmol) experienced late rejection or graft loss due to rejection compared to 15 of 92 patients (16%) with high 6-month CXCL10 levels (P = 0.008). Similar results were obtained when patients were grouped according to CXCL10-burden (cutoff, 1.06 ng/mmol). CONCLUSIONS: Six-month urinary CXCL10 is an independent predictor for long-term graft outcome and thus might be a supplementary tool to tailor surveillance strategies and therapy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".