Potential Impact of Microarray Diagnosis of T Cell–Mediated Rejection in Kidney Transplants: The INTERCOM Study
Bibliographic record
Abstract
We previously developed a microarray-based test for T cell-mediated rejection (TCMR) in a reference set of 403 biopsies. To determine the potential impact of this test in clinical practice, we undertook INTERCOM, a prospective international study of 300 indication biopsies from 264 patients (ClinicalTrials.gov NCT01299168). Biopsies from six centers-Baltimore, Barcelona, Edmonton, Hannover, Manchester and Minneapolis-were analyzed by microarrays, assigning TCMR scores by an algorithm developed in the reference set and comparing TCMR scores to local histology assessment. The TCMR score correlated with histologic TCMR lesions-tubulitis and interstitial infiltration. The accuracy for primary histologic diagnoses (0.87) was similar to the reference set (0.89). The TCMR scores reclassified 77/300 biopsies (26%): 16 histologic TCMR were molecularly non-TCMR; 15 histologic non-TCMR were molecularly TCMR, including 6 with polyoma virus nephropathy; and all 46 "borderline" biopsies were reclassified as TCMR (8) or non-TCMR (38). Like the reference set, discrepancies were primarily in situations where histology has known limitations, for example, in biopsies with scarring and inflammation/tubulitis potentially from other diseases. Neither the TCMR score nor histologic TCMR was associated with graft loss. Thus the molecular TCMR score has potential to add new insight, particularly in situations where histology is ambiguous or potentially misleading.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".