Long-term Efficacy and Safety of Piracetam in the Treatment of Progressive Myoclonus Epilepsy
Bibliographic record
Abstract
BACKGROUND: Piracetam has been proven to be effective and well tolerated in the treatment of myoclonus in short-term studies. OBJECTIVE: To assess its long-term clinical efficacy, 11 patients with disabling myoclonus due to progressive myoclonus epilepsy were treated with piracetam in an open-label study. METHODS: Neurologic outcome (at the 1st, 6th, 12th, and 18th month of treatment) was assessed by an adjusted sum score of the following 3 indices: motor impairment, functional disability, and global assessment of disability due to myoclonus. Severity of other neurologic symptoms (seizure frequency and severity, dysarthria, and gait ataxia) also was assessed. Treatment with piracetam was initiated at a dose of 3.2 g/d that was gradually increased until stable benefit was noted (maximal dose in the trial was 20 g/d). Concomitant antiepileptic drugs were maintained at their previous dose. RESULTS: Statistically significant improvement in the total rating score was observed after introduction of piracetam at the 1st, 6th, and 12th month of treatment. Severity of other neurologic symptom scores did not improve significantly. Two patients reported drowsiness during the first 2 weeks of treatment. CONCLUSIONS: Piracetam given as add-on therapy seems to be an effective, sustained, and well-tolerated treatment of myoclonus. In patients with progressive myoclonus epilepsy, the efficacy of the drug increased during the first 12 months of treatment and then stabilized.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".