Myoclonus and seizures in progressive myoclonus epilepsies: pharmacology and therapeutic trials
Bibliographic record
Abstract
Generalized motor seizures, usually tonic-clonic, tonic-vibratory, myoclonic or clonic, and stimulus-sensitive/action myoclonus are typical features of progressive myoclonus epilepsies (PMEs). Despite the introduction of many anticonvulsants, the treatment of these symptoms, particularly myoclonus, remains challenging, due to the incomplete and often transitory effects of most drugs. Moreover, treatment is only symptomatic, since therapy targeting the underlying aetiology for these genetic conditions is in its infancy. Traditional antiepileptic drugs for the treatment of PMEs are valproate, clonazepam, and phenobarbital (or primidone). These drugs may improve the overall performance of PME patients by decreasing their generalized seizures and, to a lesser extent, their myoclonic jerks. Newer drugs which have been shown to be effective include piracetam, levetiracetam, topiramate, zonisamide, and possibly perampanel for Lafora disease. The potential of other drugs (such as L-triptophan and N-acetylcysteine) and procedures (such as vagal and deep brain stimulation) has also been discussed. The available data on the efficacy of drugs are mainly based on small series or anecdotal reports. Two prospective, randomized, double blind studies investigating the novel SV2A ligand, brivaracetam, in genetically confirmed Unverricht-Lundborg patients have been performed with disappointing results. When treating PMEs, particular care should be paid to avoid drugs known to aggravate myoclonus or myoclonic seizures, such as phenytoin, carbamazepine, oxcarbazepine, lamotrigine, vigabatrin, tiagabine, gabapentin, and pregabalin. The emergency treatment of motor status, which often complicates the course of PMEs, consists of intravenous administration of benzodiazepines, valproate, or levetiracetam.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".