Clinical and Serologic Characterization of an Argentine Pediatric Myositis Cohort: Identification of a Novel Autoantibody (anti-MJ) to a 142-kDa Protein
Bibliographic record
Abstract
OBJECTIVE: Autoantibodies are frequently found in adult patients with polymyositis (PM), dermato-myositis (DM), and overlap myositis disorders. They are less common in pediatric patients with myositis. We investigated the autoantibody pattern in a pediatric Argentine Caucasian cohort to characterize novel autoantibodies. METHODS: Sera from children that satisfied published criteria for idiopathic inflammatory myopathy were analyzed for autoantibodies by RNA and protein immunoprecipitation and immunoblotting techniques. Routine myositis-specific and myositis-associated autoantibodies as well as autoantibody specificities were determined. RESULTS: We tested sera from 64 consecutive pediatric myositis patients, including 40 with juvenile DM, 7 with juvenile PM, and 17 with overlap myositis syndromes. Sixteen (25%) patients were found to have anti-MJ autoantibody exclusively, which appears to identify a subset of pediatric myositis patients with severe disease characterized by muscle contractures and atrophy and significant compromise of functional status. Fourteen (22%) patients were found to have an antibody targeting 2 proteins of 155 and 140 kDa. Other myositis-specific autoantibodies were uncommon in this pediatric cohort. CONCLUSION: A newly recognized autoantibody, anti-MJ, was the most common antibody found in this Argentine pediatric cohort. The clinical features indicated that this antibody is distinct from other reported antibodies in pediatric patients with myositis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".