Anti-interleukin 6 Therapy Effect for Refractory Joint and Skin Involvement in Systemic Sclerosis: A Real-world, Single-center Experience
Bibliographic record
Abstract
OBJECTIVE: To examine the efficacy and safety of interleukin-6 inhibition by tocilizumab (TCZ) in difficult-to-treat, real-world patients with systemic sclerosis (SSc). METHODS: Twenty-one patients (20 women; 16 diffuse cutaneous SSc; mean age: 52 ± 10 yrs; 10 with early disease [< 5 yrs]; and 11 with long-standing disease [mean disease duration 6.4 ± 3.7 yrs]) with active joint and/or skin involvement refractory to corticosteroids (n = 21), methotrexate (n = 19), cyclophosphamide (n = 10), mycophenolate mofetil (n = 7), rituximab (n = 1), leflunomide (n = 2), hydroxychloroquine (n = 2), and hematopoietic stem cell transplantation (n = 2), who received weekly TCZ (162 mg subcutaneously) in an academic center, were monitored prospectively. Changes in modified Rodnan skin score (mRSS), Disease Activity Score in 28 joints (DAS28), lung function tests (LFTs), and patient-reported outcomes (PROs) were analyzed after 1 year of treatment and at end of follow-up. RESULTS: < 0.001); LFT stabilization was observed in 16/20 patients. During the second year, 3 patients discontinued TCZ (cytomegalovirus infection in 1, inefficacy in 2) and 1 died. Beneficial effects were sustained in all 16 patients at end of follow-up (2.2 ± 1.1 yrs), except LFT deterioration in 3 patients. Apart from recurrent digital ulcer infection in 3 patients, TCZ was well tolerated. CONCLUSION: TCZ was effective in refractory joint and skin involvement regardless of SSc disease duration or subtype. Long-term retention rates and disease stabilization for most real-world patients suggest that TCZ might be a valuable choice for difficult-to-treat SSc.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".