Expansion of molecular mismatch scores to guide clinical management of kidney transplant patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: Optimizing outcomes for kidney transplant recipients requires both minimizing premature death related to over-immunosuppression and avoiding alloimmune injury associated with under-immunosuppression. Both of these goals require a precise understanding of the alloimmune risk faced by the recipient. Although the assessment of HLA mismatch at the antigen level lacks refinement, molecular mismatch has been shown to increase precision in alloimmune risk assessment. This review discusses the expansion of the role of molecular mismatch in optimizing the clinical management of kidney transplant recipients. RECENT FINDINGS: Eplet mismatch has been reported as a reliable predictive biomarker for immunosuppression adequacy and to identify recipients who are less likely to tolerate minimization or nonadherence. Human leukocyte antigen DR and/or DQ (HLA-DR/DQ) single-molecule mismatch has also been validated as a prognostic biomarker in immunosuppression conversion studies, providing a precise, individualized assessment of alloimmune risk to guide decision-making regarding immunosuppression. PIRCHE-II scores have been observed to potentiate the risk of dnDSA development. The use of molecular mismatch can also be expanded to personalized posttransplant alloimmune monitoring and dnDSA surveillance. SUMMARY: To facilitate precision medicine in transplantation, molecular mismatch has the potential to serve as a prognostic and predictive biomarker for primary alloimmunity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".