The most bothersome symptoms in neuromuscular diseases: the ERN EURO NMD Survey
Bibliographic record
Abstract
BACKGROUND: Neuromuscular diseases (NMDs) comprise a range of genetic and acquired rare disorders that affect motor neurons, peripheral nerves, neuromuscular junctions and skeletal muscles, leading to significant impairments such as muscle weakness and fatigue resulting in functional limitations. This study aims to investigate the prevalence and severity of disease-related symptoms in adult patients with NMDs registered in the European Reference Network (ERN) EURO-NMD. A cross-sectional electronic survey was conducted with 1,253 participants who reported the severity of 28 symptoms, which were scored using multi-criteria decision analysis (MCDA). RESULTS: The results identified muscle fatigue, weakness and impaired physical function/activity as the most severe and prevalent symptoms in all NMD groups, followed by coordination and/or balance problems, muscle stiffness, mental fatigue, and pain. Notably, the analysis highlighted differences in symptom severity between disease subtypes and underlined the need for standardised patient-reported outcome measures (PROMs) to address the broad heterogeneity of NMDs. CONCLUSIONS: The findings stress the critical importance of capturing patient perspectives to guide clinical care, research priorities and therapeutic development. This work argues for the development of uniform PROMs to better assess disease impact, natural history and treatment efficacy, contributing to improved patient-centred care across diverse NMD populations.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".