Significance of HLA-DQ in kidney transplantation: time to reevaluate human leukocyte antigen–matching priorities to improve transplant outcomes? An expert review and recommendations
Bibliographic record
Abstract
The weight of human leukocyte antigen (HLA) matching in kidney allocation algorithms, especially in the United States, has been devalued in a stepwise manner, supported by the introduction of modern immunosuppression. The intent was further to reduce the observed ethnic/racial disparity, as data emerged associating HLA matching with decreased access to transplantation for African American patients. In recent years, it has been increasingly recognized that a leading cause of graft loss is chronic antibody-mediated rejection, attributed to the development of de novo antibodies against mismatched donor HLA expressed on the graft. These antibodies are most frequently against donor HLA-DQ molecules. Beyond their impact on graft survival, generation of de novo donor-specific HLA antibodies also leads to increased sensitization, as measured by panel-reactive antibody metrics. Consequently, access to transplantation for patients returning to the waitlist in need of a second transplant is compromised. Herein, we address the implications of reduced HLA matching policies in kidney allocation. We highlight the observed diminished outcome data, the significant financial burden, the long-term health consequences, and, more important, the unintended consequences. We further provide recommendations to examine the impact of donor-recipient HLA class II and specifically HLA-DQα 1 β 1 mismatching, focusing on collection of appropriate data, application of creative simulation approaches, and reconsideration of best practices to reduce inequalities while optimizing patient outcomes.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".