Necrotizing Autoimmune Myopathy: A Case Report on Statin-Induced Rhabdomyolysis
Bibliographic record
Abstract
Statin-induced necrotizing myopathy (SINM) is an uncommon but severe complication associated with statin medication. SINM can develop at any point after a person starts taking steroids. It is now being acknowledged as a component of the broader category of "statin-induced myopathy." Like other immune-mediated necrotizing muscle diseases, statin-induced myositis is identified by weakness in proximal muscles, increased serum creatine kinase (CK) levels, and, in some cases, dysphagia and respiratory distress. In addition, there is evidence of muscle cell damage when examined under a microscope, occurring with minimal or no infiltration of inflammatory cells. Diagnosing SINM promptly is frequently challenging due to its unpredictable development over time, with symptoms sometimes emerging many years after the initial exposure to statins. One distinctive characteristic of SINM is the continued presence of muscle inflammation and elevated CK levels even after discontinuing statin treatment. Currently, no clinical trials are available to guide how to manage statin-induced immune-mediated necrotizing myopathy (IMNM). Here, we present a case of a 42-year-old woman diagnosed with SINM and was found to have persistently elevated CPK despite discontinuation of statins. Our case also suggests that intravenous (IV) immunoglobins and steroids are an effective and well-tolerated alternative to immunosuppressants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".