Tocilizumab (TCZ) for Giant Cell Arteritis: Clinical Outcomes Following Relapses and TCZ Discontinuation Due to Adverse Events
Bibliographic record
Abstract
OBJECTIVE: Tocilizumab (TCZ) is effective for giant cell arteritis (GCA). However, little is known regarding treatment modification and clinical outcomes after unfavorable events such as GCA relapses or TCZ discontinuation due to adverse events (AEs). METHODS: This multicenter retrospective study included patients with GCA who initiated TCZ from 2008 to 2021 at 5 Japanese hospitals. GCA relapses and TCZ-related AEs were monitored for 2 years after TCZ initiation. In patients with GCA relapses, subsequent clinical courses, including relapse symptoms and treatment modification, were followed for 90 days after the relapses. Similarly, patients who discontinued TCZ because of AEs were additionally followed until 1 year after the TCZ discontinuation to evaluate AEs, relapses, and treatment changes. RESULTS: Of 62 eligible patients, 10 patients (16%) relapsed after initiating TCZ therapy. Most relapses (8 of 10) occurred after extending TCZ intervals or discontinuing TCZ. Combinations of adjusting TCZ intervals, adjusting glucocorticoid (GC) dose, and/or adding or increasing methotrexate (MTX) therapy could manage the relapses without serious complications. In the entire cohort, AEs occurred in 28 patients (45%), and 8 patients (13%) discontinued TCZ because of AEs. After AE-related TCZ discontinuation, 6 patients attempted to taper GCs without other immunosuppressive therapy (IST), and 4 subsequently relapsed. In contrast, 2 patients who used other IST or biologic therapy could decrease GCs without relapses. CONCLUSION: Although GCA relapses can occur after initiating TCZ therapy, most relapses can be safely managed by adjusting TCZ, GC, and/or MTX doses. Adding IST or biologic treatments may potentially be related to preventing relapses when patients discontinue TCZ because of AEs.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".