The Nature of Biopsies with “Borderline Rejection” and Prospects for Eliminating This Category
Bibliographic record
Abstract
In kidney transplantation, many inflamed biopsies with changes insufficient to be called T-cell-mediated rejection (TCMR) are labeled "borderline", leaving management uncertain. This study examined the nature of borderline biopsies as a step toward eventual elimination of this category. We compared 40 borderline, 35 TCMR and 116 nonrejection biopsies. TCMR biopsies had more inflammation than borderline but similar degrees of tubulitis and scarring. Surprisingly, recovery of function after biopsy was similar in all categories, indicating that response to treatment is unreliable for defining TCMR. We studied the molecular changes in TCMR, borderline and nonrejection using microarrays, measuring four published features: T-cell burden; a rejection classifier; a canonical TCMR classifier; and risk score. These reassigned borderline biopsies as TCMR-like 13/40 (33%) or nonrejection-like 27/40 (67%). A major reason that histology diagnosed molecularly defined TCMR as borderline was atrophy-scarring, which interfered with assessment of inflammation and tubulitis. Decision tree analysis showed that i-total >27% and tubulitis extent >3% match the molecular diagnosis of TCMR in 85% of cases. In summary, most cases designated borderline by histopathology are found to be nonrejection by molecular phenotyping. Both molecular measurements and histopathology offer opportunities for more precise assignment of these cases after clinical validation.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".