Disappearance of T Cell-Mediated Rejection Despite Continued Antibody-Mediated Rejection in Late Kidney Transplant Recipients
Bibliographic record
Abstract
The prevalent renal transplant population presents an opportunity to observe the adaptive changes in the alloimmune response over time, but such studies have been limited by uncertainties in the conventional biopsy diagnosis of T cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR). To circumvent these limitations, we used microarrays and conventional methods to investigate rejection in 703 unselected biopsies taken 3 days to 35 years post-transplant from North American and European centers. Using conventional methods, we diagnosed rejection in 205 biopsy specimens (28%): 67 pure TCMR, 110 pure ABMR, and 28 mixed (89 designated borderline). Using microarrays, we diagnosed rejection in 228 biopsy specimens (32%): 76 pure TCMR, 124 pure ABMR, and 28 mixed (no borderline). Molecular assessment confirmed most conventional diagnoses (agreement was 90% for TCMR and 83% for ABMR) but revealed some errors, particularly in mixed rejection, and improved prediction of failure. ABMR was strongly associated with increased graft loss, but TCMR was not. ABMR became common in biopsy specimens obtained >1 year post-transplant and continued to appear in all subsequent intervals. TCMR was common early but progressively disappeared over time. In 108 biopsy specimens obtained 10.2-35 years post-transplant, TCMR defined by molecular and conventional features was never observed. We conclude that the main cause of kidney transplant failure is ABMR, which can present even decades after transplantation. In contrast, TCMR disappears by 10 years post-transplant, implying that a state of partial adaptive tolerance emerges over time in the kidney transplant population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".