Angiotensin II type I receptor agonistic autoantibodies are associated with poor allograft survival in liver retransplantation
Bibliographic record
Abstract
Angiotensin II type I receptor (AT1R) agonistic autoantibodies (AT1R-AA) are detrimental to kidney transplantation. Early studies suggested a similar negative effect in primary liver transplantation. Here, we studied AT1R-AA in a retrospective cohort of 94 patients who received a second liver transplant to determine their prevalence and effects. The concentrations of preformed AT1R-AA before transplantation were higher (P = .019) in the 48 patients who lost their liver grafts than in the 46 patients whose grafts survived. About half (48/94, 51.1%) of the patients were positive for AT1R-AA >17 U/mL before the second liver transplantation. In 22 (23.4%) patients, strong positive AT1R-AA (defined as >40 U/mL) were detected, of whom 16 (72.7%) patients lost their grafts. Based on Kaplan-Meier analysis, patients with strong positive AT1R-AA had significantly worse graft survival than those with AT1R-AA <40 U/mL (P = .035). In multivariate Cox models that included confounders such as sex and age, either AT1R-AA >40 U/mL (HR = 1.999 [1.085-3.682], P = .026) or increased concentrations of AT1R-AA (HR = 1.003 [1.001-1.006] per incremental U/mL, P = .019) were significantly associated with elevated risk for graft loss. In conclusion, our data indicate that there is a high prevalence of AT1R-AA in candidates for second liver transplantation and that their presence is associated with inferior long-term outcomes of the second graft.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".