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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".