The therapeutic challenge of late antibody‐mediated kidney allograft rejection
Bibliographic record
Abstract
Late antibody-mediated rejection (ABMR) is a cardinal cause of kidney allograft failure, manifesting as a continuous and, in contrast with early rejection, often clinically silent alloimmune process. While significant progress has been made towards an improved understanding of its molecular mechanisms and the definition of diagnostic criteria, there is still no approved effective treatment. In recent small randomized controlled trials, therapeutic strategies with promising results in observational studies, such as proteasome inhibitor bortezomib, anti-C5 antibody eculizumab, or high dose intravenous immunoglobulin plus rituximab, had no significant impact in late and/or chronic ABMR. Such disappointing results reinforce a need of new innovative treatment strategies. Potential candidates may be the interference with interleukin-6 to modulate B cell alloimmunity, or innovative compounds that specifically target antibody-producing plasma cells, such as antibodies against CD38. Given the phenotypic heterogeneity of ABMR, the design of adequate systematic trials to assess the safety and efficiency of such therapies, however, is challenging. Several trials are currently being conducted, and new developments will hopefully provide us with effective ways to counteract the deleterious impact of antibody-mediated graft injury. Meanwhile, the weight of evidence would suggest that, when approaching using existing treatments for established antibody-mediated rejection, "less may be more".
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".