Current status of the evaluation and management of antibody-mediated rejection in kidney transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Antibody-mediated rejection (AMR) has come to the forefront of clinical and research challenges in clinical transplantation. Despite the major progress made over the past two decades, there remains a large number of unanswered diagnostic, prognostic, and therapeutic questions. Here we review the recent studies that have helped improve our understanding of AMR. RECENT FINDINGS: Complement binding capacity of the HLA antibodies using modified single antigen bead Luminex assays to detect C1q, C4d, or C3d binding, has been associated with risk of AMR and graft failure. However, this property correlates with the antibody mean fluorescent intensity, and, in many cases, may not add additional insight. The use of molecular methods to examine expression profiles in the kidney biopsy specimens, in peripheral mononuclear cells, or urinary chemokine signature, has improved our understanding of the mechanisms of AMR-induced injury and refined our current diagnostic tools. On the treatment front, monoclonal antiinterleukin 6 receptor antibody, and C1 esterase inhibitor have shown promising results in the pilot studies but further larger trials are needed to evaluate their safety and efficacy. SUMMARY: We are making constant progress in the pursuit of a better understanding the AMR process and finding new diagnostic and therapeutic options.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.002 |
| 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".