Longterm Safety of Tocilizumab: Results from 3 Years of Followup Postmarketing Surveillance of 5573 Patients with Rheumatoid Arthritis in Japan
Bibliographic record
Abstract
OBJECTIVE: To evaluate the longterm safety of tocilizumab (TCZ) for the treatment of rheumatoid arthritis (RA) in a real-world clinical setting in Japan. METHODS: In this longterm extension of a single-arm, observational postmarketing surveillance study, a total of 5573 patients who initiated intravenous TCZ between April 2008 and July 2009 were observed for 3 years, regardless of its continuation, for incidence of fatal events, serious infections, malignancy, gastrointestinal perforations, and serious cardiac dysfunction. RESULTS: Of the 5573 patients who were enrolled, 4527 patients (81.23%) completed 3 years of followup. There were no increases in the proportions of patients with fatal events, serious infection, malignancy, GI perforation, or serious cardiac dysfunction over 3 years. The all-cause mortality rate during followup was 2.58% (0.95/100 patient-yrs), and the standardized mortality ratio was 1.27 (95% CI, 1.08 to 1.50). Patients who were older with longer disease duration and respiratory comorbidities were more likely to discontinue TCZ treatment following serious infection during the first year. Among patients who completed 3 years of TCZ treatment, serious infection developed at a constant rate during the 3-year treatment period. The proportion of malignancy during followup was 2.24% (0.83/100 patient-yrs), and the standardized incidence ratio was 0.79 (95% CI, 0.66 to 0.95). CONCLUSION: The safety profile of TCZ was consistent over time regarding mortality, serious infections, malignancy, gastrointestinal perforation, and serious cardiac dysfunction. These data confirm the longterm safety of TCZ use in patients with RA in a real-world clinical setting.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".