HLA-DR/DQ molecular mismatch: A prognostic biomarker for primary alloimmunity
Bibliographic record
Abstract
Alloimmune risk stratification in renal transplantation has lacked the necessary prognostic biomarkers to personalize recipient care or optimize clinical trials. HLA molecular mismatch improves precision compared to traditional antigen mismatch but has not been studied in detail at the individual molecule level. This study evaluated 664 renal transplant recipients and correlated HLA-DR/DQ single molecule eplet mismatch with serologic, histologic, and clinical outcomes. Compared to traditional HLA-DR/DQ whole antigen mismatch, HLA-DR/DQ single molecule eplet mismatch improved the correlation with de novo donor-specific antibody development (area under the curve 0.54 vs 0.84) and allowed recipients to be stratified into low, intermediate, and high alloimmune risk categories. These risk categories were significantly correlated with primary alloimmune events including Banff ≥1A T cell-mediated rejection (P = .0006), HLA-DR/DQ de novo donor-specific antibody development (P < .0001), antibody-mediated rejection (P < .0001), as well as all-cause graft loss (P = .0012) and each of these correlations persisted in multivariate models. Thus, HLA-DR/DQ single molecule eplet mismatch may represent a precise, reproducible, and widely available prognostic biomarker that can be applied to tailor immunosuppression or design clinical trials based on individual patient risk.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".