Prediction of Long-term Renal Allograft Outcome By Early Urinary CXCL10 Chemokine Levels
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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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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 | 0.000 | 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) | 0.000 | 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 it