Next-Generation Sequencing to Diagnose Muscular Dystrophy, Rhabdomyolysis, and HyperCKemia
Bibliographic record
Abstract
BACKGROUND: Neuromuscular disorders are a phenotypically and genotypically diverse group of diseases that can be difficult to diagnose accurately because of overlapping clinical features and nonspecific muscle pathology. Next-generation sequencing (NGS) is a high-throughput technology that can be used as a more time- and cost-effective tool for identifying molecular diagnoses for complex genetic conditions, such as neuromuscular disorders. METHODS: One hundred and sixty-nine patients referred to a Canadian neuromuscular clinic for evaluation of possible muscle disease were screened with an NGS panel of muscular dystrophy-associated genes. Patients were categorized by the reason of referral (1) muscle weakness (n=135), (2) recurrent episodes of rhabdomyolysis (n=18), or (3) idiopathic hyperCKemia (n=16). RESULTS: Pathogenic and likely pathogenic variants were identified in 36.09% of patients (61/169). The detection rate was 37.04% (50/135) in patients with muscle weakness, 33.33% (6/18) with rhabdomyolysis, and 31.25% (5/16) in those with idiopathic hyperCKemia. CONCLUSIONS: This study shows that NGS can be a useful tool in the molecular workup of patients seen in a neuromuscular clinic. Evaluating the utility of large panels of a muscle disease-specific NGS panel to investigate the genetic susceptibilities of rhabdomyolysis and/or idiopathic hyperCKemia is a relatively new field. Twenty-eight of the pathogenic and likely pathogenic variants reported here are novel and have not previously been associated with disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".