Autoantibodies in juvenile-onset myositis: Their diagnostic value and associated clinical phenotype in a large UK cohort
Bibliographic record
Abstract
OBJECTIVES: Juvenile myositis is a rare and heterogeneous disease. Diagnosis is often difficult but early treatment is important in reducing the risk of associated morbidity and poor outcomes. Myositis specific autoantibodies have been described in both juvenile and adult patients with myositis and can be helpful in dividing patients into clinically homogenous groups. We aimed to explore the utility of myositis specific autoantibodies as diagnostic and prognostic biomarkers in patients with juvenile-onset disease. METHODS: Using radio-labelled immunoprecipitation and previously validated ELISAs we examined the presence of myositis specific autoantibodies in 380 patients with juvenile-onset myositis in addition to, 318 patients with juvenile idiopathic arthritis, 21 patients with juvenile-onset SLE, 27 patients with muscular dystrophies, and 48 healthy children. RESULTS: An autoantibody was identified in 60% of juvenile-onset myositis patients. Myositis specific autoantibodies (49% patients) were exclusively found in patients with myositis and with the exception of one case were mutually exclusive and not found in conjunction with another autoantibody. Autoantibody subtypes were associated with age at disease onset, key clinical disease features and treatment received. CONCLUSIONS: In juvenile patients the identification of a myositis specific autoantibody is highly suggestive of myositis. Autoantibodies can be identified in the majority of affected children and provide useful prognostic information. There is evidence of a differential treatment approach and patients with anti-TIF1γ autoantibodies are significantly more likely to receive aggressive treatment with IV cyclophosphamide and/or biologic drugs, clear trends are also visible in other autoantibody subgroups.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".