Comparative Effectiveness of Abatacept Versus Adalimumab in Shared Epitope Positive and Negative Patients With Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: T cells is expected to be diminished by abatacept, a costimulation blocker. However, published evidence on the value of genetic stratification for abatacept treatment is conflicting. We aimed to compare the difference in effectiveness of abatacept and adalimumab in patients carrying the SE (or Val11). METHODS: The Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate is a nationwide observational cohort study recruiting patients from 53 centers across the United Kingdom before the initiation of biologic treatment and following them up prospectively for 12 months. Three hundred forty-two patients starting either abatacept or adalimumab were eligible for this analysis. Serum drug levels for abatacept, adalimumab, and methotrexate were determined at multiple time points. Multivariate modeling integrating demographic, clinical, and pharmacological variables was used to test for associations between the number of copies of the SE or Val11 and response to treatment (EULAR response; Disease Activity Score in 28 joints [DAS28] remission; change in DAS28). Differential effectiveness between drugs and genetic markers was assessed by the significance of their interaction term. RESULTS: There was no difference in the efficacy of abatacept versus adalimumab. We found weak evidence for an independent association of genetic markers with response to treatment (Val11 with EULAR response: P = 0.02), but there was no significant difference in this effect between drugs. CONCLUSION: We found no evidence that HLA typing is clinically useful to support prescription decisions for these two drugs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".