Next-Generation Sequencing to Diagnose Muscular Dystrophy, Rhabdomyolysis, and HyperCKemia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it