A molecular biopsy test based on arteriolar under-hyalinosis reflects increased probability of rejection related to under-immunosuppression
Bibliographic record
Abstract
Calcineurin inhibitor immunosuppressive drugs induce changes such as arteriolar hyalinosis (ah) in kidney transplants, raising the possibility that molecular changes in biopsies related to histologic ah can provide information about drug exposure. We hypothesized that molecular changes associated with less-than-expected hyalinosis might highlight a subpopulation of patients with under-immunosuppression/nonadherence at intermediate times of biopsy posttransplant (TxBx). Using gene expression data from 562 indication biopsies, we developed a molecular classifier for predicting the expected ah lesions (M ah ) at a particular TxBx. M ah -scores increased linearly with log(TxBx), but some biopsies had lower scores than expected for TxBx. The deviation of individual M ah -scores below the predicted regression line of M ah -scores vs TxBx is defined as "low hyalinosis index." Low hyalinosis indices were frequent in biopsies between 3 months and 3 years posttransplant, particularly among biopsies lacking histologic hyalinosis (ah0), and were associated with T cell–mediated rejection and a subset of recent-onset antibody-mediated rejection without glomerular double contours. In patients with medical records available for review, low hyalinosis indices were frequently associated with physician-recorded concerns about nonadherence (suspected or proven). We conclude that the M ah classifier and hyalinosis index identify indication biopsies with rejection for which the possibility of patient nonadherence should be considered.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".