An open-label randomized controlled trial of DMARD withdrawal in RA patients achieving therapeutic response with certolizumab pegol combined with DMARDs
Bibliographic record
Abstract
OBJECTIVES: The objective of this trial was to compare effectiveness of certolizumab pegol added to conventional synthetic DMARDs (csDMARDs) in RA patients, followed by continuing vs discontinuing background csDMARDs after treatment response. METHODS: Patients with active RA who had certolizumab pegol added to their existing csDMARD regimen due to inadequate response were eligible. At 3 or 6 months, patients who achieved a change (Δ) in DAS28 of ⩾1.2 were randomized to continue combination therapy (COMBO) or withdraw csDMARD therapy (MONO) (unblinded). The primary outcome was non-inferiority of stopping vs continuing csDMARD(s) in terms of maintaining ΔDAS28 ⩾ 1.2 or achieving DAS28 low disease activity at 18 months (non-inferiority margin: 15 percentile units). RESULTS: A total of 125 patients were enrolled, 88 randomized to COMBO (n = 43) or MONO (n = 45). No significant differences were observed between groups in baseline age, gender, race, RF status or prior biologics (16% vs 11%). Although the rate of ΔDAS28 ⩾ 1.2 and/or DAS28 low disease activity achievement at 18 months was clinically comparable between the two groups (72% vs 69%), non-inferiority assumptions were not met [absolute risk difference (upper limit of 90% CI): 2.6% (19.1%)]. Similar baseline-adjusted improvements were seen in DAS28 (COMBO vs MONO: -2.3 vs -2.1; P = 0.49) and all endpoints were not statistically different including 59% vs 56% achieved DAS28 low disease activity, 69% vs 59% ΔDAS28 ⩾ 1.2, and 41% each remission. CONCLUSION: Among RA patients achieving a therapeutic response on combination therapy with certolizumab pegol and csDMARDs, withdrawing csDMARDs was not non-inferior to maintaining csDMARDs but improvements were sustained in both groups at 18 months.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".