Effective induction therapy for anti-SRP associated myositis in childhood: A small case series and review of the literature
Bibliographic record
Abstract
BACKGROUND: Anti-Signal Recognition Particle associated myopathy is a clinically and histopathologically distinct subgroup of Juvenile Idiopathic Inflammatory Myositis, which is under-recognised in children and fails to respond to conventional first line therapies. We present three cases where remission was successfully induced using combination therapy with intensive rehabilitation. CASE PRESENTATIONS: Three new patients are reported. All 3 cases presented with profound, rapid-onset, proximal myopathy and markedly raised CK, but no rash. Histology revealed a destructive myopathy characterized by scattered atrophic and necrotic fibres with little or no inflammatory infiltrate. All 3 patients responded to induction with cyclophosphamide, IVIG and rituximab, in conjunction with intensive physiotherapy and methotrexate as the maintenance agent. Our patients regained near-normal strength (MMT > 70/80), in contrast with the current literature where >50% of cases reported severe residual weakness. A literature search on paediatric anti-SRP myositis was performed to June 2016; PubMed was screened using a combination of the following terms: signal recognition particle, autoantibodies, antibodies, myositis, muscular diseases, skeletal muscle, childhood, paediatric, juvenile. Articles in a foreign language were excluded. Nine case studies were found. CONCLUSION: This paper supports the hypothesis that anti-SRP myositis is distinct from other JIIM. It is an important differential to JDM and should be considered where there is severe weakness without rash or if highly elevated muscle enzymes (CK > 10,000 U/l) are found. Early identification is essential to initiate aggressive medical and physical therapy. Greater international collaboration and long-term follow-up data is needed to establish the most effective treatment strategy for this rare group of patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".