Specific Antinuclear Antibody Level Changes after B Cell Depletion Therapy in Systemic Sclerosis Are Associated with Improvement of Skin Thickening
Bibliographic record
Abstract
To the Editor: B cell depletion therapy using rituximab (RTX) has emerged as a promising therapy in systemic sclerosis (SSc). Our group reported a clinical benefit on skin thickening when RTX (2 × 1000 mg) in combination with methylprednisolone (100 mg) was administered at months 0 and 61. The rationale behind the use of B cell depletion in SSc treatment is based on the growing evidence that B cells play a role in the pathogenesis of the disease2,3. In addition, SSc-associated antinuclear antibody (ANA) are present in the majority of patients, but are generally accepted to be rather static markers, primarily important in diagnosis rather than in followup4. Moreover, conflicting results have been published5 on the change in disease-specific ANA titers in relation to clinical response in other connective diseases (e.g., systemic lupus erythematosus and rheumatoid arthritis) after RTX. To our knowledge, no studies in SSc are available documenting the longterm effect of RTX on these autoantibody levels. In this letter we report the 2-year serologic followup data (months 0, 3, 6, 12, 15, 18, and 24) on the same 8 patients with SSc with diffuse skin involvement (dcSSc) who were included in our initial RTX studies1,6. The idea was to evaluate the relationship between SSc-ANA level changes and clinical response. To document this latter relationship, we performed … Address correspondence to C. Bonroy, Department of Clinical Chemistry, Microbiology and Immunology, Ghent University Hospital 2P8, De Pintelaan 185, B-9000, Ghent, Belgium. E-mail: carolien.bonroy{at}uzgent.be
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".