Spectrum of Clinical Features in X-Linked Myotubular Myopathy Carriers
Bibliographic record
Abstract
Objective To characterize the spectrum of clinical features in a cohort of X-linked myotubular myopathy (XL-MTM) carriers, including prevalence, genetic features, clinical symptoms, and signs, as well as associated disease burden. Methods We performed a cross-sectional online questionnaire study among XL-MTM carriers. Participants were recruited from patient associations, medical centers, and registries in the United Kingdom, Germany, and the Netherlands. We used a custom-made questionnaire, the Checklist Individual Strength (CIS), the Frenchay Activities Index (FAI), the Short Form 12 (SF-12) health survey, and the McGill Pain Questionnaire. Carriers were classified as manifesting or nonmanifesting on the basis of self-reported ambulation and muscle weakness. Results The prevalence of manifesting carriers in this study population (n = 76) was 51%, subdivided into mild (independent ambulation, 39%), moderate (assisted ambulation, 9%), and severe (wheelchair dependent, 3%) phenotypes. In addition to muscle weakness, manifesting carriers frequently reported fatigue (70%) and exercise intolerance (49%). Manifesting carriers scored higher on the overall CIS (p = 0.001), the fatigue subscale (p < 0.001), and least severe pain subscale (p = 0.005) than nonmanifesting carriers. They scored lower on the FAI (p = 0.005) and the physical component of the SF-12 health survey (p < 0.001). Conclusions The prevalence of manifesting XL-MTM carriers may be higher than currently assumed, most having a mild phenotype and a wide variety of symptoms. Manifesting carriers are particularly affected by fatigue, limitations of daily activities, pain, and reduced quality of life. Our findings should increase awareness and provide useful information for health care providers and future clinical trials.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".