Efficacy and Safety of Belimumab in Patients with Rheumatoid Arthritis: A Phase II, Randomized, Double-blind, Placebo-controlled, Dose-ranging Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the efficacy/safety of belimumab in patients with rheumatoid arthritis (RA). METHODS: Patients fulfilling American College of Rheumatology (ACR) criteria for RA for ≥ 1 year who had at least moderate disease activity while receiving stable disease-modifying antirheumatic drug (DMARD) therapy and failed ≥ 1 DMARD were randomly assigned to placebo or belimumab 1, 4, or 10 mg/kg, administered intravenously on Days 1, 14, and 28, and then every 4 weeks for 24 weeks (n = 283). This was followed by an optional 24-week extension (n = 237) in which all patients received belimumab. Primary efficacy endpoint was the Week 24 ACR20 response. RESULTS: Week 24 ACR20 responses with placebo and belimumab 1, 4, and 10 mg/kg were 15.9%, 34.7% (p = 0.010), 25.4% (p = 0.168), and 28.2% (p = 0.080), respectively. Patients taking any belimumab dose who continued with belimumab in the open-label extension had an ACR20 response of 41% at 48 weeks. A similar ACR20 response (42%) at 48 weeks was seen in patients taking placebo who switched in the extension to belimumab 10 mg/kg. Greater response rates were observed in patients who at baseline were rheumatoid factor-positive, anticitrullinated protein antibody-positive, or tumor necrosis factor inhibitor-naive, or had elevated C-reactive protein levels, Disease Activity Score 28 > 5.1, or low B lymphocyte stimulator levels (< 0.858 ng/ml). Adverse event rates were similar across treatment groups. CONCLUSION: In this phase II trial, belimumab demonstrated efficacy and was generally well tolerated in patients with RA who had failed previous therapies. [ClinicalTrials.gov identifier NCT00071812].
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.003 |
| 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".