P.071 The value of genetics, biopsy and EMG in diagnosing congenital myopathies
Bibliographic record
Abstract
Background: Congenital myopathies (CM) are inherited muscle disorders historically classified according to features seen on muscle biopsy and congenital-onset weakness and hypotonia. The aim of our study was to evaluate the benefit of genetic testing, muscle biopsy, NCS/EMG and muscle MRI in obtaining a definite diagnosis for these patients. Methods: A retrospective chart review of all patients diagnosed at a single tertiary-care pediatric hospital over 15 years (2008-2022). REB approval was obtained. Results: Over a period of 15 years, 42 patients with CM were included. All (100%) had genetic testing (i.e. gene panel, WES), 65.9% had muscle biopsy, 67.5% had NCS/EMG and 20% had a muscle MRI. Definite diagnosis was obtained in 38% by genetic testing only, while 42.8% had a diagnosis made by genetic testing supported by the findings of one or more of the other diagnostic tools. Conclusions: Early diagnosis of CM is still essential in congenital myopathies to provide optimal care. Genetic testing is the gold standard for diagnosis, but other diagnostic tools remain valuable in the case of variants of unclear significance.
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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 | 0.002 | 0.008 |
| 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) | 0.006 | 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".