Pathologic basis of antibody-mediated organ transplant rejection
Bibliographic record
Abstract
PURPOSE OF REVIEW: Although antibody-mediated rejection of clinical organ transplants has been recognized more than a half-century ago, our understanding of its pathological/clinical phenotypes has dramatically increased over the past decade. This review highlights the pathological/clinical spectrum of ABMR and discusses its microscopic pathology in relationship with pathogenesis. RECENT FINDINGS: Newly recognized pathological manifestations of ABMR are: (1) C4d-negative active ABMR, which cannot be definitely diagnosed by current diagnostic systems and often remains underdetected. Novel molecular diagnostic tests can fill this diagnostic gap but these new tests are yet to be prepared for routine application; (2) antibody-mediated vascular rejection, which is misclassified by the current Banff Classification, is therefore inadequately treated and has a high risk for transplant failure; and (3) subclinical (insidious) microvascular inflammation, which can be with or without complement activation, predicts progression to chronic rejection, transplant dysfunction, and failure. SUMMARY: A major progress has been made in understanding of ABMR of clinical transplants in the last 5 years. New pathology types of ABMR are not appropriately classified and updates to the Banff diagnostic criteria are required. Better diagnosis would help develop effective antiantibody treatment strategies and improve long-term outcomes for patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".