Tocilizumab in Behçet Disease: A Multicenter Study of 30 Patients
Bibliographic record
Abstract
Objective To evaluate tocilizumab (TCZ) efficacy in patients with refractory Behçet disease (BD). Methods This is a multicenter study of 30 patients fulfilling the international criteria for BD and treated with TCZ at different European referral centers. The clinical response was evaluated at 6 months from TCZ initiation. Results Ninety percent of patients with BD were refractory or intolerant to anti–tumor necrosis factor (anti-TNF) agents. Overall, TCZ was effective in 25 (83%) patients with BD of whom 18 (60%) and 7 (23%) were complete and partial responders, respectively. The complete response was 67%, 60%, and 42% in patients with uveitis (18/30), neurological manifestations (5/30), and mucocutaneous and/or articular (7/30) manifestations, respectively. TCZ had a significant steroid-sparing effect allowing patients to decrease their median daily prednisone dose from 20 (IQR 10-40) mg/day to 9 (IQR 5-13) mg at 6 months (P< 0.001). The number of patients with BD needing concomitant disease-modifying antirheumatic drug therapy fell from 7 (23%) to 4 (13%) at 6 months. Mild to moderate side effects were observed in 6 (20%) patients, and 3 (10%) presented with serious adverse events (pneumonia, intestinal perforation, and septicemia) requiring therapy discontinuation in 2 cases. Conclusion TCZ seems to be an effective alternative to anti-TNF agents in treating BD-related uveitis and neurological manifestations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".