Effectiveness and Safety of Tocilizumab: Postmarketing Surveillance of 7901 Patients with Rheumatoid Arthritis in Japan
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it