Hyalinosis Lesions in Renal Transplant Biopsies: Time-Dependent Complexity of Interpretation
Bibliographic record
Abstract
Because calcineurin inhibitor (CNI) immunosuppressive drugs induce arteriolar hyalinosis (ah) in kidney transplants, ah lesions can potentially provide information about drug exposure. We studied the relationship of ah lesions to findings and outcomes in 562 indication biopsies taken 3 days to 35 years after transplant. Prevalence of ah lesions increased with time of biopsy after transplant (TxBx). The ah scores correlated with arterial intimal thickening and atrophy-fibrosis but, unlike atrophy-fibrosis, did not increase until after 500 days because of a background of ah1 lesions in early biopsies reflecting donor aging. Correlation of ah scores with other features varied with TxBx-in early biopsies, donor age and related changes, and in very late biopsies, chronic antibody-mediated rejection and glomerulonephritis and associated lesions. After correction for TxBx, ah0 in intermediate time periods was associated with increased risk of T cell-mediated rejection and graft loss, probably because of underimmunosuppression and nonadherence. Thus, ah lesions in indication biopsies have multiple associations: donor age (early, usually ah1), chronic glomerular diseases (late, often ah2/3), and adequate exposure to CNIs at intermediate times. This threefold TxBx-dependent complexity must be considered when interpreting indication biopsies: ah lesions often indicate adequate CNI exposure, not toxicity, and unexpected ah0 should increase vigilance for nonadherence and underimmunosuppression.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".