Novel Scoring System for Ranking Hematopoietic Stem Cell Transplantation
Bibliographic record
Abstract
BACKGROUND: When human leukocyte antigen (HLA)-matched donors are not available for hematopoietic stem cell transplants (HSCT), there are no well-accepted guidelines for ranking 7/8 HLA-matched unrelated donors to achieve optimal transplant outcomes. A novel scoring system for ranking HLA mismatches for these donors was investigated. METHODS: High-resolution HLA types were used to determine amino acid mismatches located in the HLA antigen-recognition domain. The location and physicochemical properties of mismatched amino acids were used to assign scores for peptide binding, T-cell receptor docking, and HLA structure/function. The scores were tested using a cohort of 2319 patients with leukemia or myelodysplastic syndrome who received their first unrelated donor transplant using conventional graft-versus-host disease (GVHD) prophylaxis between 2000 and 2014. Donors were 7/8 HLA-matched with a single HLA Class I mismatch. Primary outcomes were overall survival and acute GVHD. RESULTS: The scores did not significantly (p < 0.01) associate with transplant outcomes, although a Peptide Score = 0 (i.e., no differences in peptide binding; N = 146, 6.3%) appears to have lower transplant-related mortality (TRM) compared to higher scores (p = 0.019). HLA mismatches with Peptide Score = 0 were predominately HLA-C*03:03/03:04 (62%), previously reported to be a permissive mismatch, and a group of 28 other HLA mismatches (38%) that showed similar associations with TRM. CONCLUSIONS: This study suggests that HLA mismatches that do not alter peptide binding or orientation (Peptide Score = 0) could expand the number of permissive HLA mismatches. Further investigation is needed to confirm this observation and to explore alternative scoring systems for ranking HLA mismatched donors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".