Polymorphisms and pharmacogenomics for the toxicity of methotrexate monotherapy in patients with rheumatoid arthritis
Bibliographic record
Abstract
BACKGROUND: Methotrexate (MTX) is widely used and considered a first-line disease modifying antirheumatic drug (DMARD) for the treatment of rheumatoid arthritis (RA). However, 10% to 30% of patients discontinue therapy within a year of starting the treatment, usually because of undesirable side effects. Many of the relevant genes have been investigated to estimate the association between gene polymorphisms and MTX toxicity in RA patients, although inconsistent results have been reported. METHODS: We searched EMBASE and PubMed in February 2016 for polymorphisms and pharmacogenomics study of the toxicity of MTX monotherapy in RA patients. The meta-analysis was stratified by whether genetic variants associated with MTX toxicity. RESULTS: A total of 42 publications that included 28 genes with 88 gene SNPs associated with the transporters, enzymes, and metabolites of MTX or the progression of RA were included in the SR, and 31 studies were included in 7 meta-analyses. The meta-analysis showed a significant association between the toxicity of MTX and the RFC-1 80G > A (rs1051266) polymorphism in the European RA patients. CONCLUSION: RFC-1 80G > A (rs1051266) polymorphism was associated with MTX toxicity, and larger and more stringent study designs may provide more accurate results for the effect of these SNPs on the MTX toxicity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".