Natural Antibodies Are Associated With Rejection and Long-term Renal Allograft Loss in a Multicenter International Cohort
Bibliographic record
Abstract
BACKGROUND: Potentially harmful nonhuman leukocyte antigen antibodies have been identified in renal transplantation, including natural immunoglobulin G antibodies (Nabs) reactive to varied antigenic structures, including apoptotic cells. METHODS: In this retrospective, multicenter study, we assessed Nabs by reactivity to apoptotic cells in sera collected from 980 kidney transplant recipients across 4 centers to determine their association with graft outcomes. RESULTS: Elevated pretransplant Nabs were associated with graft loss (hazard ratio [HR] 2.71; 95% confidence interval [CI], 1.15-6.39; P = 0.0232), the composite endpoint of graft loss or severe graft dysfunction (HR 2.40; 95% CI, 1.13-5.10; P = 0.0232), and T cell-mediated rejection (odds ratio [OR] 1.77; 95% CI, 1.07-3.02; P = 0.0310). High pretransplant Nabs together with donor-specific antibodies (DSAs) were associated with increased risk of composite outcomes (HR 6.31; 95% CI, 1.81-22.0; P = 0.0039). In patients with high pretransplant Nabs, the subsequent development of posttransplant Nabs was associated with both T cell-mediated rejection (OR 3.64; 95% CI, 1.61-8.36; P = 0.0021) and mixed rejection (OR 3.10; 95% CI, 1.02-9.75; P = 0.0473). Finally, elevated pre- and posttransplant Nabs combined with DSAs were associated with increased risk of composite outcomes (HR 3.97; 95% CI, 1.51-10.43; P = 0.0052) and T cell-mediated rejection (OR 7.28; 95% CI, 2.16-25.96; P = 0.0016). CONCLUSIONS: The presence of pre- and posttransplant Nabs, together with DSAs, was associated with increased risk of poor graft outcomes and rejection after renal transplantation.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".