Genetic Myopathies Initially Diagnosed and Treated as Inflammatory Myopathy
Bibliographic record
Abstract
OBJECTIVES: Differentiating genetic myopathies from inflammatory myopathies can be challenging because of multiple overlapping clinical features. Examples are presented to highlight important clinical features that assist in the differentiation between the two. METHODS: Clinical features including age at onset, history, pattern of weakness, serum creatine kinase activity, electromyography findings, and muscle biopsies are reported in six patients initially thought to have an inflammatory myopathy in whom the final diagnosis was a genetic myopathy. RESULTS: All six patients met Bohan and Peter criteria for at least probable idiopathic polymyositis and were subsequently found to have a genetic myopathy (4 DYSF, RYR1, and GNE). The key distinguishing clinical were minimal to no response to immunosuppression and atypical involvement of distal muscles in the majority of cases. CONCLUSIONS: Patients diagnosed with inflammatory myopathies should be reevaluated for the possibility of a genetic myopathy if they fail to respond to a course of disease-modifying agents and/or there is atypical distal muscle involvement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".