Effect of disease duration and prior disease-modifying antirheumatic drug use on treatment outcomes in patients with rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVES: To determine if disease duration and number of prior disease-modifying antirheumatic drugs (DMARDs) affect response to therapy in patients with established rheumatoid arthritis (RA). METHODS: Associations between disease duration or number of prior DMARDs and response to therapy were assessed using data from two randomised controlled trials in patients with established RA (mean duration, 11 years) receiving adalimumab+methotrexate. Response to therapy was assessed at week 24 using disease activity outcomes, including 28-joint Disease Activity Score based on C-reactive protein (DAS28(CRP)), Simplified Disease Activity Index (SDAI) and Health Assessment Questionnaire Disability Index (HAQ-DI), and proportions of patients with 20%/50%/70% improvement in American College of Rheumatology (ACR) responses. RESULTS: In the larger study (N=207), a greater number of prior DMARDs (>2 vs 0-1) was associated with smaller improvements in DAS28(CRP) (-1.8 vs -2.2), SDAI (-22.1 vs -26.9) and HAQ-DI (-0.43 vs -0.64) from baseline to week 24. RA duration of >10 years versus <1 year was associated with higher HAQ-DI scores (1.1 vs 0.7) at week 24, but results on DAS28(CRP) and SDAI were mixed. A greater number of prior DMARDs and longer RA duration were associated with lower ACR response rates at week 24. Data from the second trial (N=67) generally confirmed these findings. CONCLUSIONS: Number of prior DMARDs and disease duration affect responses to therapy in patients with established RA. Furthermore, number of prior DMARDs, regardless of disease duration, has a limiting effect on the potential response to adalimumab therapy.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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".