Retention of triple therapy with methotrexate, sulfasalazine, and hydroxychloroquine compared to combination methotrexate and leflunomide in rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVE: There are various combination conventional synthetic disease-modifying-antirheumatic drug (csDMARD) treatment strategies used in rheumatoid arthritis (RA). A commonly used csDMARD combination is triple therapy with methotrexate (MTX), sulfasalazine (SSZ) and hydroxychloroquine (HCQ). Another approach is double therapy with MTX and leflunomide (LEF). We compared the real-world retention of these two treatment combinations. METHODS: Patients with RA from the Ontario Best Practices Research Initiative (OBRI) who received triple or double therapy on or after OBRI enrolment were included. Retention rates were compared between these two groups. We also analyzed which medication in the combination was discontinued and the reasons for treatment discontinuation. Disease activity was assessed at baseline, 6 and 12 months after treatment initiation as well as at time of discontinuation. Risk factors for treatment discontinuation were also examined. RESULTS: Six hundred and ninety-two patients were included (258 triple and 434 double therapy). There were 175 (67.8%) discontinuations in the triple therapy group and 287 (66.1%) discontinuations in patients on double therapy. The median survival for triple therapy was longer (15.1 months; 95% CI: 11.2-21.2) compared to double therapy (9.6 months; 95%CI: 7.03-12.2). However, this was not statistically significant. Disease activity at 6 and 12 months, measured by 28-joint count Disease Activity Score based on erythrocyte sedimentation rate (DAS28-ESR) was lower with triple therapy (mean DAS28 at 6 months 3.4 vs. 3.9, P<0.0001 and at 12 months 3.2 vs. 3.5, P=0.0005). CONCLUSION: Patients on triple therapy remained on treatment longer than patients on double therapy. However, this difference was not statistically significant.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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) | 0.001 | 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".