A Probabilistic Approach to Histologic Diagnosis of Antibody-Mediated Rejection in Kidney Transplant Biopsies
Bibliographic record
Abstract
Histologic diagnosis of antibody-mediated rejection (ABMR) in kidney transplant biopsies uses lesion score cutoffs such as 0 versus >0 rather than actual scores and requires donor-specific antibody (DSA); however, cutoffs lose information, and DSA is not always reliable. Using microarray-derived molecular ABMR scores as a histology-independent estimate of ABMR in 703 biopsies, we reassessed criteria for ABMR to determine relative importance of various lesions, the utility of equations using actual scores rather than cutoffs, and the potential for diagnosing ABMR when DSA is unknown or negative. We confirmed that the important features for ABMR diagnosis were peritubular capillaritis (ptc), glomerulitis (g), glomerular double contours, DSA and C4d staining, but we questioned some features: arterial fibrosis, vasculitis, acute tubular injury, and sum of ptc+g scores. Regression equations using lesion scores predicted molecular ABMR more accurately than score cutoffs (area under the curve 0.85-0.86 vs. 0.75). DSA positivity improved accuracy, but regression equations predicted ABMR with moderate accuracy when DSA was unknown. Some biopsies without detectable DSA had high probability of ABMR by regression, although most had HLA antibody. We concluded that regression equations using lesion scores plus DSA maximized diagnostic accuracy and can estimate probable ABMR when DSA is unknown or undetectable.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".