The Role of Muscle Imaging in the Diagnosis and Assessment of Children with Genetic Muscle Disease
Bibliographic record
Abstract
Abstract Muscle magnetic resonance imaging (MRI) and ultrasound (US) are emerging tools to assist in the diagnosis of children with genetic muscle disease. Increasing number of studies demonstrate that these imaging techniques can identify selective patterns of muscle atrophy, fatty degeneration, and muscle edema that help to distinguish between different early-onset genetic myopathies and muscular dystrophies. Recognizing patterns of pathology by muscle imaging can help to guide genetic testing and avoid the more invasive procedure of a muscle biopsy. Conversely, since massive parallel sequencing is now more commonly used as the initial step in diagnostic testing, imaging techniques can help to confirm or exclude if a variant of uncertain significance is indeed disease causing and compatible with a pattern of pathology as detected by muscle imaging. Whereas for diagnostic purposes and pattern recognition, muscle pathology does not need to be quantified, measuring disease progression is increasingly supported by quantitative muscle imaging, which is critical given the recent increment in rare disease therapeutic trials. Here, we discuss the value of muscle imaging techniques in pediatric muscle disease and summarize data identifying specific patterns of involvement in muscle MRI and US in some of the more common genetic myopathies and muscular dystrophies.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".