Update on Pharmacological Treatment of Progressive Myoclonus Epilepsies
Bibliographic record
Abstract
BACKGROUND: Progressive myoclonus epilepsies (PMEs) are a group of rare inherited diseases featuring a combination of myoclonus, seizures and variable degree of cognitive impairment. Despite extensive investigations, a large number of PMEs remain undiagnosed. In this review, we focus on the current pharmacological approach to PMEs. METHODS: References were mainly identified through PubMed search until February 2017 and backtracking of references in pertinent studies. RESULTS: The majority of available data on the efficacy of antiepileptic medications in PMEs are primarily anecdotal or observational, based on individual responses in small series. Valproic acid is the drug of choice, except for PMEs due to mitochondrial diseases. Levetiracetam and clonazepam should be considered as the first add-on treatment. Zonisamide and perampanel represent promising alternatives. Phenobarbital and primidone should be reserved to patients with resistant disabling myoclonus or seizures. Lamotrigine should be used with caution due to its unpredictable effect on myoclonus. Avoidance of drugs known to aggravate myoclonus and seizures, such as carbamazepine and phenytoin, is paramount. Psychiatric (in particular depression) and other comorbidities need to be adequately managed. Although a 3- to 4-drug regimen is often necessary to control seizures and myoclonus, particular care should be paid to avoid excessive pharmacological load and neurotoxic side effects. Target therapy is possible only for a minority of PMEs. CONCLUSIONS: Overall, the treatment of PMEs remains symptomatic (i.e. pharmacological treatment of seizures and myoclonus). Further dissection of the genetic background of the different PMEs might hopefully help in the future with individualised treatment options.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".