Molecular Diagnostics Supporting a ≥35% Diffuse Peritubular Capillaritis Extent Threshold for Diagnosis of AMR—A Retrospective Dual Center Study
Bibliographic record
Abstract
Peritubular capillaritis (ptc) is a hallmark lesion of antibody-mediated rejection (AMR), but the grading of its extent is historically based on arbitrary defined cut-offs. Molecular AMR diagnosis via intragraft gene expression measurements may provide evidence to challenge established ptc categories. We retrospectively included 38 renal allograft biopsies from clinical routine, performed because of suspicion of AMR. Biopsies were re-assessed by an experienced nephropathologist and intragraft gene expression was measured using the NanoString nCounter® platform. Ptc categories were correlated with AMR gene expression to identify a ptc extent cut-off with optimal prediction of molecular diagnosis of AMR [gene expression levels above first quartile (AMRQ>1)]. Finally, an independent validation cohort (n = 25, Erasmus MC, Rotterdam, The Netherlands) was included to reproduce the results. Re-assessment of biopsies revealed AMR in 26/68.4%, mixed rejection in 5/13.2%, and T-cell-mediated rejection in 3/7.9%. Biopsies with diffuse ptc had significantly higher AMR gene expression compared to biopsies with focal ptc and biopsies with no ptc (64.0/53.3–84.0 vs. 31.5/27.0–49.5, p = 0.023 and 27.0/14.3–31.8, p = 0.003, median/IQR). Sensitivity analysis revealed that a ≥35% ptc cut-off resulted in higher AUCs for predicting AMRQ>1 compared to ptc50% (AUC 0.78, 95% CI: 0.63–0.93, p = 0.009 versus AUC: 0.74, CI: 0.56–0.90, p = 0.03). In the validation cohort, only the ptc35–, but not the ptc50%, cut-off significantly predicted AMRQ>1 (AUC 0.75, 95% CI: 0.54–0.96 p = 0.04 vs. AUC 0.69, CI: 0.46–0.93, p = 0.13). Using intragraft gene expression measurement, we identified a new ptc extent threshold with better prediction of molecular AMR. The newly proposed cut-off of ≥35% could potentially improve diagnostic evaluation and prognostication in cases with suspected or diagnosed AMR.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".