Relapse Risk and Safety of Long-Term Tocilizumab Use Among Patients With Giant Cell Arteritis: A Single-Enterprise Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the safety and efficacy of tocilizumab (TCZ) in giant cell arteritis (GCA) in a large North American cohort. METHODS: Patients with GCA treated with TCZ between January 1, 2010, and May 15, 2020, were retrospectively identified. Kaplan-Meier methods were used to estimate time to TCZ discontinuation and time to first relapse after TCZ discontinuation. Poisson regression models were used to compare annualized relapse rates before, during, and after TCZ use. Age- and sex-adjusted risk factors associated with relapse on and off TCZ and development of adverse events of significant interest (AESIs) were examined using Cox models. RESULTS: < 0.001) but increased to 0.64 relapses/person-year after TCZ discontinuation. Fifty-two patients stopped TCZ after a median of 16.8 months; 27 relapsed after discontinuation (median: 8.4 months; 58% relapsed within 12 months). Only 14.9% of patients stopped TCZ because of AESIs. Neither dose/route of TCZ, presence of large-vessel vasculitis, nor duration of TCZ therapy prior to discontinuation predicted relapse after TCZ stop. CONCLUSION: TCZ is well tolerated in GCA, with low rates of discontinuation for AESIs. However, relapse occurred in > 50% despite median treatment > 12 months. Since the duration of TCZ prior to discontinuation did not significantly affect subsequent risk of GCA recurrence, further research is needed to determine the optimal duration of therapy.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".