P.042 Anti-HMG Coenzyme A reductase antibody (anti-HMGCR) myopathy: case review of two pediatric patients from a single center
Bibliographic record
Abstract
Background: Necrotizing anti-HMGCR myopathy is rare in children. Pediatric cases are not typically associated with statin use or malignancy. Methods: Retrospective chart review (January 2009 to December 2023) identified cases of anti-HMGCR myopathy at our hospital. Results: Two patients were identified. Patient A, presented at 8 yo with a 2 year history of proximal muscle weakness. His CK was 4,840 U/L (normal <205 U/L) with a high anti-HMGCR antibody titre. His Childhood Myositis Assessment Scale (CMAS) score was 33/52. Monthly IVIG was started and his muscle strength and CK improved. Two years later, weekly methotrexate was started for persistent mild CK elevation (602 to 869 U/L). At 11 years old, 3 years after diagnosis, his CMAS score was 47 and he could participate in soccer with mild fatigue. Patient B, presented at 8 yo with acute proximal weakness, rash and CMAS 13/52. His CK was 20,185 U/L with elevated anti-HMGCR antibody titre. He received oral corticosteroids, weekly methotrexate and monthly IVIG. At 10 yo, 2 years after diagnosis, he is asymptomatic with CMAS 51. He is maintained on methotrexate monotherapy. Conclusions: Anti-HMGCR antibody myopathy requires prompt diagnosis to obviate muscle necrosis and long-term complications. Our patients showed clinical and CMAS improvement with treatment.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.003 | 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".