A New Diagnostic Algorithm for Antibody-Mediated Microcirculation Inflammation in Kidney Transplants
Bibliographic record
Abstract
We studied the significance of microcirculation inflammation in kidney transplants, including 329 indication biopsies from 251 renal allograft recipients, who were mostly nonpresensitized (crossmatch negative). Glomerulitis (g) and peritubular capillaritis (ptc) were often associated with antibody-mediated rejection (65% and 75%, respectively), but were also found in other diseases in the absence of donor-specific antibody (DSA): T-cell-mediated rejection (ptc, g), glomerulonephritis (g) and acute tubular necrosis (ptc). To develop rules for reducing the nonspecificity of microcirculation inflammation and defining the best grading thresholds associated with DSA, we built and validated a decision tree to predict DSA. The decision tree revealed that g + ptc sum (addition of g-score plus ptc-score) was the best predictor of DSA, followed by time posttransplant, then C4d, which had a small role. Late biopsies with g + ptc > 0 showed higher frequency of DSA compared to early biopsies with g + ptc > 0 (79% vs. 27%). Microcirculation inflammation in early biopsies was often false positive (antibody-independent). The decision tree predicted DSA with higher sensitivity and accuracy than C4d staining. Microcirculation inflammation sum score predicted graft failure independently of time, C4d and transplant glomerulopathy. Thus any degree of microcirculation inflammation in late kidney transplant biopsies strongly indicates presence of DSA and predicts progression to graft failure.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".