221.5: Advancing renal transplant patient care: Unveiling eplet mismatch thresholds for precise risk assessment and prognosis.
Bibliographic record
Abstract
Introduction: Enhancing renal transplant patient care necessitates a comprehensive understanding of immunological compatibility between donors and recipients. Traditional antigen-matching methods have limitations in accurately predicting rejection risk and graft survival. However, recent advancements in high-resolution HLA epitope analysis offer a promising avenue for precise risk assessment. Methods: In this study, we delved into the Brazilian Eplet registry database to investigate the intricate relationship between eplet mismatches and donor-specific antibody (DSA) formation in 626 renal transplant patients. Leveraging high-resolution HLA data obtained through sequencing or predictive algorithms, we meticulously examined eplet mismatch thresholds at various HLA loci. Results: Our analysis uncovered a notable prevalence of DSA formation against HLA-DQ Z(60 patients), alongside a diverse spectrum of eplet mismatch loads across the patient cohort. Significantly, we identified eplet mismatch cut-offs at HLA-A, B, C, and DQ loci associated with increased DSA formation risk (15, 11, 9, 9, p<0.05). Survival analysis further underscored the clinical relevance of these thresholds, highlighting marked differences in DSA-free survival among risk-stratified groups (P<0.01). Conclusion: Eplet mismatch thresholds offer a refined approach to risk assessment post-transplant, surpassing the limitations of conventional antigen matching. Integration of these thresholds into clinical practice holds immense potential for tailoring transplant management strategies and providing patients with accurate prognoses. This study marks a significant advancement in renal transplant care, poised to transform patient outcomes and elevate standards of post-transplant management.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".