Treatment of Refractory Anti-HMG-CoA Reductase Myopathy: A Role for Rituximab?
Bibliographic record
Abstract
Anti-hydroxymethylglutaryl CoA reductase (HMGCR) myopathy is a subtype of myositis characterized by proximal muscle weakness, elevated serum creatine kinase (CK) levels, and autoantibodies recognizing HMGCR1. While statins are an established risk factor for developing anti-HMGCR myopathy in older patients, some individuals develop this condition without a known statin exposure2,3. To date, effective treatment strategies have not been established in clinical trials. Nonetheless, many patients with anti-HMGCR myopathy improve with immunosuppressive therapy, and current expert opinion guidelines recommend initiating treatment with corticosteroids, methotrexate, and/or intravenous immunoglobulin (IVIG)1. Unfortunately, a significant number of patients with anti-HMGCR myopathy have persistently active disease despite aggressive treatment with these and other agents. Indeed, a study including 50 patients with anti-HMGCR myopathy treated for 2 years or more found that 30% continued to have weakness and elevated muscle enzymes4. This underscores the importance of finding more effective treatment modalities for these patients. A number of observations suggest the possibility that autoantibodies may play a pathogenic role in anti-HMGCR myopathy. For example, anti-HMGCR titers have been … Address correspondence to Dr. A.L. Mammen, National Institutes of Health, 50 South Drive, Room 1141, Building 50, MSC 8024, Bethesda, Maryland 20892, USA. E-mail: andrew.mammen{at}nih.gov.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".