MS arising during Tocilizumab therapy for rheumatoid arthritis
Bibliographic record
Abstract
BACKGROUND: Interleukin-6 (IL6) blockage is a treatment strategy used in many inflammatory conditions. Trials in Neuromyelitis Optica Spectrum Disorder (NMOSD) are ongoing. Secondary auto-immunity affecting the central nervous system (CNS) is well described with some biologic agents, mainly tumor necrosis factor (TNF)-alpha inhibitors. These treatments can also aggravate patients with known multiple sclerosis (MS). OBJECTIVES: To describe a case of a patient who developed MS using another biologic, IL6 receptor antibody Tocilizumab. RESULTS: A 48-year-old woman developed MS while on treatment with Tocilizumab for Rheumatoid Arthritis (RA). This is the first published report of this association. It has obvious implications for NMOSD patients receiving anti-IL6 therapy. Development of new white matter lesions suggestive of MS in a patient treated with anti-IL6 therapy might represent an important complication of therapy. CONCLUSION: This case illustrates that Tocilizumab might cause secondary auto-immunity in CNS. It is important to be aware of this potential complication as anti-IL6 therapy might become an option for the treatment NMOSD.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".