Targeting interleukin-6 pathways in giant cell arteritis management: A narrative review of evidence
Bibliographic record
Abstract
Giant cell arteritis (GCA) is a chronic inflammatory vasculitis with a significant impact on vascular and patient health. It may present with non-specific symptoms and can lead to severe complications if not managed effectively. This narrative review explores the treatment of GCA with interleukin-6 (IL-6) pathway inhibitors, focusing on key studies from selected databases published between 2018 and 2024. The findings reveal that the current treatment primarily involves glucocorticoids (GCs), but their long-term use is associated with adverse effects. Targeting the IL-6 pathway offers therapeutic benefits by reducing inflammation and sparing GC use. Tocilizumab, a humanized immunoglobulin G1κ monoclonal antibody that blocks the IL-6 receptor, has demonstrated efficacy in achieving sustained remission and improving quality of life in people with GCA. However, challenges remain in understanding the optimal duration of therapy, managing relapse upon discontinuation, and addressing long-term structural vascular outcomes. Additional research is needed to further elucidate the complex pathogenesis of GCA and to optimize treatment strategies to achieve sustained remission both clinically and histologically while minimizing adverse effects. This review provides a comprehensive overview of the evidence of IL-6 inhibition in GCA management, highlighting both its therapeutic benefits and the challenges associated with its use.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".