Defining Relationships Among Tests for Kidney Transplant Antibody-Mediated Rejection
Bibliographic record
Abstract
Introduction: Effective therapies for kidney transplant antibody-mediated rejection (ABMR) will require accurate diagnoses plus assessment of ABMR activity, and the new tests that were used to show treatment effects in the clinical trial such as donor-derived cell-free DNA (dd-cfDNA) and molecular biopsy analysis (the Molecular Microscope Diagnostic System) could be useful. Methods: Trifecta-Kidney (ClinicalTrials.gov #NCT04239703) studied 717 indication biopsies to define the relationships among the following 4 tests used for ABMR: (i) standard-of-care (SOC) local histologic biopsy ABMR diagnosis, (ii) MMDx ABMR diagnosis, (iii) dd-cfDNA, and (iv) donor-specific antibody (DSA). Results: All 4 tests were correlated in a partial correlation network, with a hierarchy of intertest correlations: MMDx ABMR > dd-cfDNA > histology ABMR > DSA. Surprisingly, DSA correlated at least as strongly with MMDx ABMR as with histologic ABMR, even though DSA is not used in MMDx. When expressed in the same 6 rejection classes, MMDx diagnosed ABMR more frequently than histology. When histology disagreed with MMDx ABMR, dd-cfDNA and DSA correlated more strongly with MMDx assessment. However, histology also detected ABMR lesions in some cases that MMDx called No Rejection, correlating with subthreshold molecular ABMR activity, dd-cfDNA and DSA (AJT 25:72-87, 2024). Molecular rejection predicted graft outcomes better than histologic rejection in Trifecta-Kidney, and this finding was confirmed in the earlier INTERCOMEX study cohort. Discussion: The 4-way intertest correlations extend below current thresholds for diagnosing ABMR. These results map a network of 4 ABMR-related tests that can add precision to ABMR assessment in trials and clinical management, and highlight the need to establish the clinical significance of subthreshold ABMR activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".