Low Hydrophobic Mismatch Scores Calculated for HLA-A/B/DR/DQ Loci Improve Kidney Allograft Survival
Bibliographic record
Abstract
We evaluated the impact of human leukocyte antigen (HLA) disparity (immunogenicity; IM) on long-term kidney allograft survival. The IM was quantified based on physicochemical properties of the polymorphic linear donor/recipient HLA amino acids (the Cambridge algorithm) as a hydrophobic, electrostatic, amino acid mismatch scores (HMS\AMS\EMS) or eplet mismatch (EpMM) load. High-resolution HLA-A/B/DRB1/DQB1 types were imputed to calculate HMS for primary/re-transplant recipients of deceased donor transplants. The multiple Cox regression showed the association of HMS with graft survival and other confounders. The HMS integer 0-10 scale showed the most survival benefit between HMS 0 and 3. The Kaplan-Meier analysis showed that: the HMS=0 group had 18.1-year median graft survival, a 5-year benefit over HMS>0 group; HMS ≤ 3.0 had 16.7-year graft survival, a 3.8-year better than HMS>3.0 group; and, HMS ≤ 7.8 had 14.3-year grafts survival, a 1.8-year improvement over HMS>7.8 group. Stratification based on EMS, AMS or EpMM produced similar results. Additionally, the importance of HLA-DR with/without -DQ IM for graft survival was shown. In our simulation of 1,000 random donor/recipient pairs, 75% with HMS>3.0 were re-matched into HMS ≤ 3.0 and the remaining 25% into HMS≥7.8: after re-matching, the 13.5 years graft survival would increase to 16.3 years. This approach matches donors to recipients with low/medium IM donors thus preventing transplants with high IM donors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".