Methotrexate in Giant Cell Arteritis Deserves a Second Chance — A High-dose Methotrexate Trial Is Needed
Bibliographic record
Abstract
Giant cell arteritis (GCA) is a chronic inflammatory disease of the large- to medium-sized arteries and shows a relapsing course in up to 75% of patients. In sharp contrast to other autoimmune/inflammatory diseases, the treatment of GCA still heavily relies on high-dose, longterm glucocorticoids (GC)1. After 5 years of GC treatment, > 50% of patients still have active disease and are continuing the treatment. The well-known side effects of GC add to the burden of the disease itself, decreasing the quality of life of these elderly patients with GCA. Are there good alternatives for GC in the treatment of GCA? Recently, a successful randomized controlled trial (RCT) was performed with the interleukin (IL-) 6 receptor blocker tocilizumab (TCZ) in GCA. More than 50% of the treated patients reached the primary endpoint of the study and were in sustained GC-free remission at 1 year2. Although TCZ is an important addition to the therapeutic tools against GCA, there are also several drawbacks. First, almost 50% of patients still develop a relapse despite TCZ. Second, under treatment with TCZ, one cannot rely on the acute-phase reactants as biomarkers of disease activity in GCA. Currently there are no IL-6–independent validated biomarkers for routine use in GCA. Also, as with other new treatments with potential therapeutic effect in GCA, the costs of TCZ are significant. In contrast to the upcoming trials with new, expensive treatment modalities in GCA (upadacitinib NCT03725202, ustekinumab NCT03711448, NCT02955147, sarilumab NCT03600805, baricitinib NCT03026504, granulocyte-macrophage colony-stimulating factor blockade), … Address correspondence to Dr. E. Brouwer, Department of Rheumatology and Clinical Immunology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands. E-mail: e.brouwer{at}umcg.nl
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".