More precise donor–recipient matching: the role of eplet matching
Bibliographic record
Abstract
PURPOSE OF REVIEW: A precise understanding of the alloimmune risk faced by individual recipients at the time of transplant is an unmet need in transplantation. Although conventional HLA donor-recipient mismatch is too imprecise to fulfil this need, HLA molecular mismatch increases the precision in alloimmune risk assessment by quantifying the difference between donors and recipients at the molecular level. RECENT FINDINGS: Within each conventional HLA mismatch the number, type, and position of mismatched amino acids create a wide range of HLA molecular mismatches between recipients and donors. Multiple different solid organ transplant groups from across the world have correlated HLA molecular mismatch with transplant outcomes including de novo donor-specific antibody development, antibody-mediated rejection, T-cell-mediated rejection, and allograft survival. SUMMARY: All alloimmunity is driven by differences between donors and recipients at the molecular level. HLA molecular mismatch may represent an advancement compared to traditional HLA antigen mismatch as a fast, reproducible, cost-effective way to improve alloimmune risk assessment at the time of transplantation to move the field towards precision medicine.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".