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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".