Provisional practice recommendation for the management of myopathy in <scp>VCP</scp>‐associated multisystem proteinopathy
Bibliographic record
Abstract
Valosin-containing protein (VCP)-associated multisystem proteinopathy (MSP) is a rare genetic disorder with abnormalities in the autophagy pathway leading to various combinations of myopathy, bone diseases, and neurodegeneration. Ninety percent of patients with VCP-associated MSP have myopathy, but there is no consensus-based guideline. The goal of this working group was to develop a best practice set of provisional recommendations for VCP myopathy which can be easily implemented across the globe. As an initiative by Cure VCP Disease Inc., a patient advocacy organization, an online survey was initially conducted to identify the practice gaps in VCP myopathy. All prior published literature on VCP myopathy was reviewed to better understand the different aspects of management of VCP myopathy, and several working group sessions were conducted involving international experts to develop this provisional recommendation. VCP myopathy has a heterogeneous clinical phenotype and should be considered in patients with limb-girdle muscular dystrophy phenotype, or any myopathy with an autosomal dominant pattern of inheritance. Genetic testing is the only definitive way to diagnose VCP myopathy, and single-variant testing in the case of a known familial VCP variant, or multi-gene panel sequencing in undifferentiated cases can be considered. Muscle biopsy is important in cases of diagnostic uncertainty or lack of a definitive pathogenic genetic variant since rimmed vacuoles (present in ~40% cases) are considered a hallmark of VCP myopathy. Electrodiagnostic studies and magnetic resonance imaging can also help rule out disease mimics. Standardized management of VCP myopathy will optimize patient care and help future research initiatives.
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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 | 0.015 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.015 |
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".