Multiple Single-Nucleotide Polymorphism–Based Risk Model for Clinical Outcomes After Allogeneic Stem-Cell Transplantation, Especially for Acute Graft-Versus-Host Disease
Bibliographic record
Abstract
BACKGROUND: We aimed to develop multiple single-nucleotide polymorphism (SNP)-based risk models associated with the risk of transplant outcomes including graft-versus-host disease (GVHD). METHODS: The study evaluated 259 SNPs in 53 genes in 394 pairs of donors and recipients. In a discovery set (n=307 receiving related donor transplantation), overall survival, relapse-free survival (RFS), nonrelapse mortality, and acute or chronic GVHD were evaluated. RESULTS: Eight recipients' SNPs of IL2, IL6R, FAS, EDN1, TGFB1, and NFKBIA genes and 12 donors' SNPs of NOS1, IL1B, TGFB2, NOD2/CARD15, TNFRII, IL1R1, and FCGR2A genes were identified in univariate analyses. Risk models were generated using significant clinical variables and genetic SNP markers after filtering out through multivariate analyses. Then, we divided patients into four quartiles (25%, Q) according to their risks. The final models stratified patients into low-risk (Q1), moderate-risk (Q2, Q3), and high-risk (Q4) groups in terms of overall survival (P<0.0001), RFS (P<0.0001), nonrelapse mortality (P=0.0043), and acute GVHD (P<0.0001), but not for chronic GVHD (P=0.763). External validation was performed in 87 transplant pairs that received matched unrelated donor transplantation, especially for RFS (P=0.016) and acute GVHD (P=0.027). CONCLUSION: Risk models can improve prognostic stratification of patients according to their risk for transplant outcome.
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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) | 0.001 | 0.001 |
| 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".