GP.03 Diagnostic yield of next generation sequencing and myositis autoantibody panels in patients with axial myopathy
Bibliographic record
Abstract
Background: Axial myopathy is a rare neuromuscular disorder of variable etiology characterised by preferential involvement of the paraspinal muscles. We reviewed clinical features of patients with axial myopathies and the diagnostic yield of myositis-associated antibodies and targeted next generation sequencing panels. Methods: We performed a retrospective review of patients presenting with axial myopathy at the Montreal Neurological Hospital from 2011-2018. Data collection included clinical presentation, disease course, results of electromyography, imaging, laboratory and genetic testing, and histopathology on muscle biopsy. Results: Twenty-five patients were identified. Initial manifestation of axial weakness was head drop (15), camptocormia (8), and rigid spine (2). Autoimmune myositis was diagnosed in 9 patients, seropositive in 7 out of 7 tested for myositis-associated antibodies. Genetic testing was consistent with oculopharyngeal muscular dystrophy in one patient and RYR-1 (ryanodine receptor 1) related core myopathy in another. Local radiotherapy or spine surgery preceded the onset of axial weakness in 1 and 6 patients, respectively. Muscle biopsies were available in 17 patients and revealed myopathic changes (16), inflammatory changes (6), and myopathy with vacuoles (3). Conclusions: Recent advancements in genetic and antibody testing, combined with paraspinal muscle biopsy, allow for more precise classification and identification of potentially treatable axial myopathies.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".