Single-Arm Study of Etanercept in Adult Patients with Moderate to Severe Rheumatoid Arthritis Who Failed Adalimumab Treatment
Bibliographic record
Abstract
INTRODUCTION: To evaluate the efficacy and safety of etanercept treatment in adult patients with moderate to severe rheumatoid arthritis (RA) who failed to respond (primary failure) or lost a satisfactory response (secondary failure) to adalimumab. METHODS: All patients discontinued prior adalimumab treatment and continued methotrexate with etanercept 50 mg once weekly for 24 weeks. The primary study endpoint was American College of Rheumatology 20% improvement criteria (ACR20) at week 12. RESULTS: Eighty-five patients (mean age 56.6 years; female 80.0%) were evaluated for safety and 84 for efficacy. Thirty (35.7%) patients achieved ACR20 at week 12; the lower bound of the 95% confidence interval (CI; 25.6, 46.9) was greater than the prespecified goal of 24% based on previous research. Improvements from baseline in clinical outcomes and patient-reported outcomes were observed at each study visit. In planned subgroup analyses, patients with anti-adalimumab antibodies and secondary adalimumab failure had the highest ACR20 response to etanercept at week 12 (11/17 patients; 64.7%). Among the patients with secondary adalimumab failure, those with anti-adalimumab antibodies were fivefold more likely to have an ACR20 response to etanercept than those without anti-adalimumab antibodies (odds ratio 5.2; 95% CI 2.0, 13.5; P < 0.001). Adverse events were reported for 62 (72.9%) patients and were consistent with previous studies of etanercept. Most adverse events were mild or moderate in severity. CONCLUSION: Switching to etanercept is a therapeutic option in patients with RA who fail adalimumab treatment. The presence of anti-adalimumab antibodies may provide additional support for switching to etanercept, particularly in patients with secondary adalimumab failure. TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT01927757.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".