Better understanding of transplant glomerulopathy secondary to chronic antibody-mediated rejection
Bibliographic record
Abstract
Transplant glomerulopathy (TG) is generally accepted to result from repeated episodes of endothelial activation, injury and repair, leading to pathological abnormalities of double contouring or multi-layering of the glomerular basement membrane. TG is a major sequel of chronic active antibody-mediated rejection (cABMR), from pre-existing or de novo anti-HLA antibodies. Hepatitis C infection, thrombotic microangiopathy or other factors may also contribute to TG development. TG prevalence is 5-20% in most series, reaching 55%, in some high-risk cohorts, and is associated with worse allograft outcomes. Despite its prevalence and clinical significance, few well-studied treatment options have been proposed. Similar to desensitization protocols, plasmapheresis with or without immunoabsorption, high-dose intravenous immunoglobulin, rituximab, bortezomib and eculizumab have been proposed in the treatment of TG due to cABMR individually or in various combinations. Robust clinical trials are urgently needed to address this major cause of allograft loss. This review summarizes the current knowledge of the epidemiology, etiology, pathology, and the preventive and treatment options for TG secondary to cABMR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".