1142 Mexiletine is an effective treatment in non-dystrophic myotonia
Bibliographic record
Abstract
The Non-Dystrophic Myotonias (NDM) are a group of skeletal muscle channelopathies caused by mutations in the chloride channel gene, CLCN1 or sodium channel gene, SCN4A. They can cause disabling stiffness, fatigue and weakness. There is anecdotal evidence that mexiletine may reduce stiffness but no randomised, double-blind, placebo-controlled trials have been done to date. We conducted an international, randomised, double-blind, placebo-controlled, crossover trial of 59 subjects with NDM. Participants were randomised to mexiletine 200 mg tds or placebo for 4 weeks, followed by a 1-week wash out, and then crossover to the alternate treatment for 4 weeks. Efficacy of the drug was monitored by daily patient reported symptom severity (stiffness, pain, weakness, tiredness) on a scale of 1–9. Improvement in stiffness was the primary outcome measure. Secondary outcome measures included a clinical assessment, quality of life, quantitative grip myotonia assessment and neurophysiological measurements of short and long exercise testing and myotonia on needle EMG. Mexiletine was found to significantly improve stiffness compared with placebo with a mean difference of 2.73 (p<0.0001) compared with placebo. It also significantly improved pain, weakness, fatigue and quality of life. It was well tolerated with no serious cardiac events. Gastrointestinal side effects were reported by 15%. Mexiletine is therefore an effective and safe drug in the treatment of stiffness in NDM.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".