Comprehensive Analysis of Transcript Changes Associated With Allograft Rejection: Combining Universal and Selective Features
Bibliographic record
Abstract
We annotated the top transcripts associated with kidney transplant rejection by p-value, either universal for all rejection or selective for T cell-mediated rejection (TCMR) or antibody-mediated rejection (ABMR; ClinicalTrials.gov NCT01299168). We used eight class-comparison algorithms to interrogate microarray results from 703 biopsies, 205 with rejection. The positive comparators were all rejection, TCMR, or ABMR; the negative comparators varied from normal biopsies to all nonrejecting biopsies, including other diseases. The universal algorithm, rejection versus all nonrejection, identified transcripts mainly inducible by interferon γ. Selectivity for ABMR or TCMR required the other rejection class as well as nonrejection biopsies in the comparator to avoid selecting universal transcripts. Direct comparison of ABMR versus TCMR yielded only transcripts related to TCMR, the stronger signal. Transcripts highly associated with rejection were never completely specific for rejection: Many were increased in biopsies without rejection, reflecting sharing between rejection and injury-induced innate immunity. Union of the top 200 transcripts from universal and selective algorithms yielded 454 transcripts that permitted unsupervised analysis of biopsies in principal component analysis: PC1 was rejection, and PC2 was separation of TCMR from ABMR. Appreciating rejection-associated molecular changes requires a diverse case mix, accurate histologic classification (including C4d-negative ABMR), and both selective and universal algorithms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".