Effectiveness of Tumor Necrosis Factor Inhibitors in Combination with Various csDMARD in the Treatment of Rheumatoid Arthritis: Data from the DREAM Registry
Bibliographic record
Abstract
OBJECTIVE: To analyze and compare the effectiveness and drug survival in patients with rheumatoid arthritis, as measured by 28-joint Disease Activity Score (DAS28) and Health Assessment Questionnaire-Disability Index (HAQ-DI), of tumor necrosis factor inhibitor (TNFi) monotherapy, TNFi + leflunomide (LEF), TNFi + sulfasalazine (SSZ), TNFi + other conventional synthetic disease-modifying antirheumatic drugs (csDMARD), and TNFi + methotrexate (MTX) therapy, in daily practice. METHODS: Data were collected from the DREAM registry. Patients beginning their first TNFi treatment were included in the study: TNFi monotherapy (n = 320), TNFi + SSZ (n = 103), TNFi + LEF (n = 80), TNFi + other csDMARD (n = 99), TNFi + MTX alone (n = 919), TNFi + MTX + other csDMARD (n = 412). Treatment effectiveness was analyzed using DAS28 and HAQ-DI with linear mixed models and the TNFi drug survival was analyzed using Kaplan-Meier curves and Cox regression. All analyses have been corrected for confounders. RESULTS: The patients who received TNFi + MTX had significantly better DAS28 and HAQ-DI values over time (both p < 0.001) and longer TNFi drug survival than TNFi monotherapy (p < 0.001). TNFi + SSZ and TNFi + other csDMARD had significantly better DAS28 values over time (p = 0.001) and longer drug survival (p = 0.001) versus TNFi monotherapy. TNFi + LEF was not significantly better compared to monotherapy. Adding other csDMARD to the TNFi + MTX combination provided no added value. CONCLUSION: Preferably, TNFi should be prescribed together with MTX. If this is not possible, we advise the use of other csDMARD.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".