Molecular Diagnosis of T Cell-Mediated Rejection in Human Kidney Transplant Biopsies
Bibliographic record
Abstract
Histologic diagnosis of T cell-mediated rejection is flawed by subjective assessments, nonspecific lesions and arbitrary rules. This study developed a molecular test for T cell-mediated rejection. We used microarray results from 403 kidney transplant biopsies to derive a classifier assigning T cell-mediated rejection scores to all biopsies, and compared these with histologic assessments. The score correlated with histologic lesions of T cell-mediated rejection (infiltrate, tubulitis). The accuracy of the classifier for the histology diagnoses was 89%. Very high and low molecular scores corresponded with unanimity among three pathologists on the presence or absence of T cell-mediated rejection, respectively. The molecular score had low sensitivity (50%) and positive predictive value (62%) for the histology diagnoses. However, histology showed similar disagreement between pathologists--only 45-56% sensitivity of one pathologist with diagnoses of T cell-mediated rejection by another. Discrepancies between molecular scores and histology were mostly when histology was ambiguous ("borderline") or unreliable, e.g. in cases with scarring or inflammation induced by tissue injury. Vasculitis (isolated v-lesion TCMR) was particularly discrepant, with most cases exhibiting low TCMR scores. We propose new rules to integrate molecular tests and histology into a precision diagnostic system that can reduce errors, ambiguity and interpathologist disagreement.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".