Delays in Tocilizumab Therapy for Patients With Giant Cell Arteritis in the United States
Bibliographic record
Abstract
Objective Despite the high risk for permanent vision loss in elderly individuals with giant cell arteritis (GCA), initiation of subcutaneous tocilizumab (TCZ) is often delayed. We used chart review for GCA patients prescribed subcutaneous TCZ to investigate delays in drug initiation. Methods We included 82 patients with GCA at the University of Washington prescribed subcutaneous TCZ between 2017 and 2024. Time from medication request to medication approval/start and cost of TCZ were compared by insurance payor using 1-way ANOVA. Use of copay assistance, prior authorization requirement, drug manufacturer/foundation medication coverage, and switches to intravenous (IV) TCZ were compared by insurance using Pearson chi-square or Fisher exact tests. Results For all patients with GCA, the mean time between request and first dose was 43 days; the mean time between request and insurance approval was 17 days, and the mean time between medication approval and medication start was 30 days. Patients with Medicare or Medicare Advantage paid significantly more out-of-pocket for the first month of TCZ ($1399 vs $823,P< 0.01) and had significantly higher rates of copay assistance (P< 0.01) and full coverage of medication by the drug manufacturer or foundation (P= 0.04). Conclusion Patients with GCA experienced significant delays in starting TCZ therapy. In addition, patients on Medicare or Medicare Advantage plans had significantly higher out-of-pocket costs compared to other patients. These delays and costs are excessive for a vulnerable population with a potentially disabling disease. Further research is needed to investigate causes of delays, the high cost of medication, and effects on clinical outcomes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".