Detection of Clinical and Subclinical Tubulo-Interstitial Inflammation by the Urinary CXCL10 Chemokine in a Real-Life Setting
Bibliographic record
Abstract
Urinary CXCL10 is a promising noninvasive biomarker for tubulo-interstitial allograft inflammation, but its diagnostic characteristics have not been assessed in a real-life setting. We investigated urinary CXCL10 in 213 consecutive renal allograft recipients having 362 surveillance biopsies at 3/6 months and 80 indication biopsies within the first year posttransplant. Allograft histology results were classified as (i) acute Banff score zero, (ii) interstitial infiltrates only, (iii) tubulitis t1, (iv) tubulitis t2-3 and (v) isolated vascular compartment inflammation. For clinical and subclinical pathologies, urinary CXCL10 correlated well with the extent of tubulo-interstitial inflammation. To determine diagnostic characteristics of urinary CXCL10, histological groups were separated into two categories: no relevant inflammation (i.e. acute Banff score zero and interstitial infiltrates only) versus all other pathologies (i.e. tubulitis t1-3 and isolated vascular compartment inflammation). For subclinical pathologies, AUC was 0.69 (sensitivity 61%, specificity 72%); for clinical pathologies, AUC was 0.74 (sensitivity 63%, specificity 80%). A urinary CXCL10-guided biopsy strategy would have reduced performance of surveillance and indication biopsies by 61% and 64%, respectively. Missed (sub)clinical pathologies were mostly tubulitis t1 and isolated vascular compartment lesions. In real life, urinary CXCL10 had clinically useful diagnostic properties making it a candidate biomarker to guide allograft biopsies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".