Rituximab in the Treatment of Jo1 Antibody–associated Antisynthetase Syndrome: Anti-Ro52 Positivity as a Marker for Severity and Treatment Response
Bibliographic record
Abstract
OBJECTIVE: Rituximab (RTX) has been used successfully for the treatment of severe Jo1 antibody-associated antisynthetase syndrome. The aim of this retrospective study was to evaluate the effect of RTX in severe Jo1 antisynthetase syndrome and determine predictive factors for response. METHODS: There were 61 patients with Jo1 antisynthetase syndrome identified; 18 of these received RTX. One patient was lost to followup. The remaining 17 patients and 30 out of 43 patients who were treated with conventional immunosuppressive (IS) drugs were followed for a mean of 35 months and 84 months, respectively. RESULTS: Polymyositis/dermatomyositis (95%) and interstitial lung disease (ILD; 66%) were the dominant clinical manifestations. Detection of anti-Ro52 antibodies (43%) was significantly associated with acute-onset ILD (p = 0.016) with O2 dependency, and patients with high concentrations of anti-Ro52 (20%) had the highest risk (p = 0.0005). Sixteen out of 18 patients (89%) showed a fast and marked response to RTX. Among those patients who were highly positive for anti-Ro52, response to RTX was seen in 7 out of 7 cases (100%), but no response to cyclophosphamide (n = 4), cyclosporine A (n = 3), azathioprine (n = 9), methotrexate (n = 5), or leflunomide (n = 2) was observed. One patient treated with RTX died of pneumonia. CONCLUSION: RTX is effective in the treatment of severe forms of Jo1 antisynthetase syndrome. In our retrospective study, the presence of high anti-Ro52 antibody concentrations predicts severe acute-onset ILD and nonresponse to IS drugs. In contrast to conventional IS, RTX is equally effective in patients with Jo1 antisynthetase syndrome, independent of their anti-Ro52 antibody status.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".