Identifying Subphenotypes of Antibody-Mediated Rejection in Kidney Transplants
Bibliographic record
Abstract
The key lesions in antibody-mediated kidney transplant rejection (ABMR) are microcirculation inflammation (peritubular capillaritis and/or glomerulitis lesions, abbreviated "pg") and glomerular double contours (cg lesions). We used these features to explore subphenotypes in 164 indication biopsies with ABMR-related diagnoses: 137 ABMR (109 pure and 28 mixed with T cell-mediated rejection [TCMR]) and 27 transplant glomerulopathy (TG), identified from prospective multicenter studies. The lesions indicated three ABMR subphenotypes: pgABMR, cgABMR, and pgcgABMR. Principal component analysis confirmed these subphenotypes and showed that TG can be reclassified as pgcgABMR (n = 17) or cgABMR (n = 10). ABMR-related biopsies included 45 pgABMR, 90 pgcgABMR, and 25 cgABMR, with four unclassifiable. Dominating all time intervals was the subphenotype pgcgABMR. The pgABMR subphenotype presented earliest (median <2 years), frequently mixed with TCMR, and was most associated with nonadherence. The cgABMR subphenotype presented late (median 9 years). Subphenotypes differed in their molecular changes, with pgABMR having the most histologic-molecular discrepancies (i.e. potential errors). Donor-specific antibody (DSA) was not identified in 29% of pgcgABMR and 46% of cgABMR, but failure rates and molecular findings were similar to cases where DSA was known to be positive. Thus, ABMR presents distinct subphenotypes, early pg-dominant, late cg-dominant, and combined pgcg phenotype, differing in time, molecular features, accompanying TCMR, HLA antibody, and probability of nonadherence.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".