Discrepancy analysis comparing molecular and histology diagnoses in kidney transplant biopsies
Bibliographic record
Abstract
Discrepancy analysis comparing two diagnostic platforms offers potential insights into both without assuming either is always correct. Having optimized the Molecular Microscope Diagnostic System (MMDx) in renal transplant biopsies, we studied discrepancies within MMDx (reports and sign-out comments) and between MMDx and histology. Interpathologist discrepancies have been documented previously and were not assessed. Discrepancy cases were classified as "clear" (eg, antibody-mediated rejection [ABMR] vs T cell-mediated rejection [TCMR]), "boundary" (eg, ABMR vs possible ABMR), or "mixed" (eg, Mixed vs ABMR). MMDx report scores showed 99% correlations; sign-out interpretations showed 7% variation between observers, all located around boundaries. Histology disagreed with MMDx in 37% of biopsies, including 315 clear discrepancies, all with implications for therapy. Discrepancies were distributed widely in all histology diagnoses but increased in some scenarios; for example, histology TCMR contained 14% MMDx ABMR and 20% MMDx no rejection. MMDx usually gave unambiguous diagnoses in cases with ambiguous histology, for example, borderline and transplant glomerulopathy. Histology lesions or features associated with more frequent discrepancies (eg, tubulitis, arteritis, and polyomavirus nephropathy) were not associated with increased MMDx uncertainty, indicating that MMDx can clarify biopsies with histologic ambiguity. The patterns of histology-MMDx discrepancies highlight specific histology diagnoses in which MMDx assessment should be considered for guiding therapy.
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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 | 0.034 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".