Effectiveness and Safety of Tocilizumab: Postmarketing Surveillance of 7901 Patients with Rheumatoid Arthritis in Japan
Bibliographic record
Abstract
OBJECTIVE: An all-patient postmarketing surveillance program was conducted to evaluate the safety and effectiveness of tocilizumab (TCZ) for rheumatoid arthritis (RA) in the real-world clinical setting in Japan. METHODS: Patients received 8 mg/kg TCZ every 4 weeks and were observed for 28 weeks. Data were collected on patient characteristics, and drug safety and effectiveness. RESULTS: A total of 7901 patients were enrolled. Percentages of total and serious adverse events (AE) were 43.9% and 9.6%, respectively. The most common serious AE were infections (3.8%). Logistic regression analysis identified the following risk factors for the development of serious infection: age ≥ 65 years, disease duration ≥ 10 years, previous or concurrent respiratory disease, and concomitant corticosteroid dose > 5 mg/day (prednisolone equivalent). The incidence rate of serious infections in patients with ≥ 3 risk factors was 11.2%, compared with 1.2% for patients without risk factors. The Week 28 rates of 28-joint Disease Activity Score-erythrocyte sedimentation rate remission, Boolean remission, and European League Against Rheumatism (EULAR) Good Response were 47.6%, 15.1%, and 59.4%, respectively. Contributing factors for effectiveness were body weight ≥ 40 kg, less advanced RA, no previous biologics, no concomitant corticosteroids or nonsteroidal antiinflammatory drugs, and low disease activity at baseline. From the benefit-risk balance analysis, patients with a high probability of remission and a low probability of developing serious infection were most likely to have less advanced RA and to have not received biologics previously. CONCLUSION: These data confirm the safety and effectiveness of TCZ in patients with RA in the real-world clinical setting in Japan and identify factors that contribute to the successful use of TCZ for RA.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".