Real Time Central Assessment of Kidney Transplant Indication Biopsies by Microarrays: The INTERCOMEX Study
Bibliographic record
Abstract
The authors conducted a prospective trial to assess the feasibility of real time central molecular assessment of kidney transplant biopsy samples from 10 North American or European centers. Biopsy samples taken 1 day to 34 years posttransplantation were stabilized in RNAlater, sent via courier overnight at ambient temperature to the central laboratory, and processed (29 h workflow) using microarrays to assess T cell- and antibody-mediated rejection (TCMR and ABMR, respectively). Of 538 biopsy samples submitted, 519 (96%) were sufficient for microarray analysis (average length, 3 mm). Automated reports were generated without knowledge of histology and HLA antibody, with diagnoses assigned based on Molecular Microscope Diagnostic System (MMDx) classifier algorithms and signed out by one observer. Agreement between MMDx and histology (balanced accuracy) was 77% for TCMR, 77% for ABMR, and 76% for no rejection. A classification tree derived to provide automated sign-outs predicted the observer sign-outs with >90% accuracy. In 451 biopsy samples where feedback was obtained, clinicians indicated that MMDx more frequently agreed with clinical judgment (87%) than did histology (80%) (p = 0.0042). In 81% of feedback forms, clinicians reported that MMDx increased confidence in management compared with conventional assessment alone. The authors conclude that real time central molecular assessment is feasible and offers a useful new dimension in biopsy interpretation. ClinicalTrials.gov NCT#01299168.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.001 | 0.001 |
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".