Addressing The Unmet Need For Precision Diagnosis: Potential Impact of Molecular Assessments On Conventional Biopsy Diagnostics in 41% of 700 Indication Biopsies.
Bibliographic record
Abstract
When transplants develop problems, treatment depends critically on accurate assessment of antibody-mediated and T cell-mediated rejection (ABMR, TCMR). But conventional assessments have problems, including the poor reproducibility of pathologist opinions e.g. 50% sensitivity for TCMR diagnoses in rejecting biopsies, and variability in DSA measurement. The addition of objective molecular measurements could add precision and guide therapy. We compared conventional and molecular assessments in 700 indication biopsies, prospectively collected 3 days to 31 years post transplant between 2004-13, including 291 from INTERCOM, 242 from Genome Canada, 81 from Hopital St Louis, Paris, and 86 new biopsies from INTERCOM centers. All were diagnosed conventionally and by the Molecular Microscope (MM) system using new Affymetrix U219 microarrays. The U219 classifiers were built using Genome Canada and INTERCOM samples with published methods. MM diagnoses strongly correlated with conventional diagnoses (table 1) but offered many new insights.Table: No Caption available.In 104 conventional ABMR, MM found 58 ABMR, 5 TCMR, 6 mixed, and 35 with no rejection (accuracy 82%). In 25 called TG but not ABMR, MM found 9 ABMR and 1 TCMR. In 77 conventional TCMR, MM found 37 TCMR, 8 mixed, 6 ABMR, and 9 with no rejection (accuracy 84%). In 32 with conventional mixed, MM found 24 (75%) were not mixed. MM assigned categories to 104 biopsies where conventional assessment was explicitly indeterminate, including 35 called suspicious for ABMR and all 79 Borderline. Moreover, in 20 with BK, MM found TCMR in 11. In 306 conventionally assessed as no rejection or BK, the MM found 26 ABMR, 12 TCMR, and 2 mixed. Thus in 285 (41%) of 700 biopsies, the addition of MM scores for ABMR and TCMR had potential to change management e.g. finding TCMR in BK cases, making diagnoses in “borderline” and “suspicious” cases, and suggesting that most “mixed” is not mixed. Thus molecular diagnosis, while not necessarily always correct, adds a new dimension to the understanding of troubled transplants in critical situations, addressing a major unmet need. DISCLOSURE:Halloran, P.: Other, Astellas, Lecturing, One Lambda, Lecturing, Novartis, Lecturing.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 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".