Antibody-mediated rejection: prevention, monitoring and treatment dilemmas
Bibliographic record
Abstract
PURPOSE OF REVIEW: Antibody-mediated rejection (AMR) has emerged as the leading cause of late graft loss in kidney transplant recipients. Donor-specific antibodies are an independent risk factor for AMR and graft loss. However, not all donor-specific antibodies are pathogenic. AMR treatment is heterogeneous due to the lack of robust trials to support clinical decisions. This review provides an overview and comments on practical but relevant dilemmas physicians experience in managing kidney transplant recipients with AMR. RECENT FINDINGS: Active AMR with donor-specific antibodies may be treated with plasmapheresis, intravenous immunoglobulin and corticosteroids with additional therapies considered on a case-by-case basis. On the contrary, no treatment has been shown to be effective against chronic active AMR. Various biomarkers and prediction models to assess the individual risk of graft failure and response to rejection treatment show promise. SUMMARY: The ability to personalize management for a given kidney transplant recipient and identify treatments that will improve their long-term outcome remains a critical unmet need. Earlier identification of AMR with noninvasive biomarkers and prediction models to assess the individual risk of graft failure should be considered. Enrolling patients with AMR in clinical trials to assess novel therapeutic agents is highly encouraged.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".