Double Pretransplant Positivity for Autoantibodies to LG3 and Angiotensin II Type 1 Receptor Is Associated With Alloimmune Vascular Injury in Kidney Transplant Recipients
Bibliographic record
Abstract
Both angiotensin II receptor autoantibodies (ATRabs) and autoantibodies to LG3 have been linked to kidney graft rejection with alloimmune vascular injury (AVI). We aimed to examine whether positivity for both anti-LG3 and ATRabs is associated with rejection with AVI in kidney transplant recipients. Methods: We performed a retrospective cohort study including consecutive kidney transplant recipients between 2013 and 2017 at a single center. The primary outcome was acute rejection with AVI (Banff grade 2 or 3 T-cell-mediated rejection and/or antibody-mediated rejection) in the first 3 mo posttransplant. The secondary outcome was death-censored allograft loss. The independent variables, anti-LG3 and ATRab, were measured pretransplant. Results: Among the 328 study participants, 68 experienced acute rejection with AVI and 23 experienced graft loss over a median follow-up of 4.5 y. In a multivariable model, double pretransplant positivity for anti-LG3/ATRab was associated with acute rejection with AVI (odds ratio: 2.73, 95% confidence interval: 1.06-7.05). We did not observe an association between double positivity for anti-LG3/ATRab and death-censored graft loss. Conclusions: Double positivity for anti-LG3/ATRabs pretransplant is associated with a higher risk of acute rejection with AVI. Whether therapies that remove antibodies could decrease that risk remains to be studied.Supplemental Visual Abtract: http://links.lww.com/TXD/A494.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".