Human leukocyte antigen mismatch and precision medicine in transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pretransplant and posttransplant alloimmune risk assessment needs to evolve towards a precision medicine model already used in other areas of medicine. Although this has not been possible with traditional risk factors available at the time of transplant, new methods of human leukocyte antigen (HLA) molecular mismatch have generated hope that alloimmune risk assessment may be precise enough for personalized treatment strategies. RECENT FINDINGS: This review describes the various HLA molecular mismatch methods and some of the recent publications for each method. These include studies that have evaluated HLA molecular mismatch in the context of lung, pancreas and kidney transplant as a correlate with short and long-term outcomes. The limitations of traditional alloimmune risk assessment strategies are highlighted in the context of individualized patient care. CONCLUSION: Recent studies that have evaluated HLA molecular mismatch in the context of immunosuppression minimization are examples of how more precise measurements of alloimmune risk can lead to novel insights that may help personalize immunosuppression protocols.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".