A Comparison of HLA Molecular Mismatch Methods to Determine HLA Immunogenicity
Bibliographic record
Abstract
BACKGROUND: Antibody-mediated rejection is a major cause of premature graft loss in kidney transplantation. Multiple scoring systems are available to assess the HLA mismatch between donors and recipients at the molecular level; however, their correlation with the development of de novo donor-specific antibody (dnDSA) has not been compared in recipients on active immunosuppression. METHODS: HLA-DRβ1/3/4/5/DQα1β1 molecular mismatch was determined using eplet analysis, amino acid mismatch, and electrostatic mismatch for 596 renal transplant recipients and correlated with HLA-DR/DQ dnDSA development. The molecular mismatch scores were evaluated in multivariate models of posttransplant dnDSA-free survival. RESULTS: Eplet mismatch correlated with amino acid mismatch and electrostatic mismatch (R = 0.85-0.96). HLA-DR dnDSA-free survival correlated with HLA-DR eplet mismatch (hazards ratio [HR], 2.50 per 10 eplets mismatched; P < 0.0001), amino acid mismatch (HR, 1.49 per 10 amino acids mismatched; P < 0.0001), and electrostatic mismatch (HR, 1.23 per 10 units mismatched; P < 0.0001). HLA-DQ dnDSA-free survival correlated with HLA-DQ eplet mismatch (HR, 1.98 per 10 eplets mismatched; P < 0.0001), amino acid mismatch (HR, 1.24 per 10 amino acids mismatched; P < 0.0001), and electrostatic mismatch (HR, 1.14 per 10 units mismatched; P < 0.0001). All 3 methods were significant multivariate correlates of dnDSA development after adjustment for recipient age, baseline immunosuppression, and nonadherence. CONCLUSIONS: HLA molecular mismatch represents a precise method of alloimmune risk assessment for renal transplant patients. The method used to determine the molecular mismatch is likely to be driven by familiarity and ease of use as highly correlated results are produced by each method.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.002 | 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".