Longterm Safety and Efficacy of Tocilizumab in Patients with Rheumatoid Arthritis: A Cumulative Analysis of Up to 4.6 Years of Exposure
Bibliographic record
Abstract
OBJECTIVE: To assess the longterm safety and efficacy of tocilizumab (TCZ) in patients with moderate to severe rheumatoid arthritis (RA). METHODS: Patient data were from 5 randomized controlled TCZ trials (n = 4211), their open-label extension phases (n = 3512), and a drug interaction study (n = 23). All randomly assigned patients, regardless of previous RA treatment, were analyzed. Measures of safety included number of adverse events (AE), serious AE (SAE), AE leading to treatment discontinuation, laboratory tests, and deaths. Efficacy measures included American College of Rheumatology (ACR) 20/50/70 responses, tender joint count (TJC), swollen joint count (SJC), ACR core set components, and low disease activity (LDA) or Disease Activity Score in 28 joints (DAS28) remission. ACR/European League Against Rheumatism (EULAR) disease remission was a posthoc exploratory analysis. RESULTS: Total duration of observation was 12,293 patient-years (PY). No new safety signals were identified; infections were the most common AE and SAE. The rate of serious infections was 4.5/100 PY. Improvements from baseline in clinical efficacy, measured as ACR20/50/70 responses, TJC, SJC, ACR core set components, and LDA and DAS28 remission, were generally sustained through at least 216 weeks of followup. ACR/EULAR disease remission was attained by 16.5% (Boolean) and 22.7% (index) of patients at Week 216. CONCLUSION: TCZ has to date been studied for up to 4.6 years (240 weeks) of treatment in patients with RA. Our analysis reveals a longer-term safety profile consistent with previous observations, no new safety signals, and durable efficacy of TCZ in a large clinical trial program.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".