Seizure management and prescription patterns of anticonvulsants in Dravet syndrome: A multicenter cohort study from Germany and review of literature
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to describe the treatment pattern of patients with Dravet syndrome (DS) in Germany with routine antiepileptic drugs (AEDs) and emergency medication, and to review the literature of real-world evidence on medicine utilization of patients with DS in Europe. METHODS: Patient use of routine AEDs and emergency medications over 3-6 months was analyzed from a 2018 multicenter survey of 93 caregivers of patients with DS throughout Germany. Results were contextualized in a review of real-world evidence on medicine utilization of patients with DS in Europe. RESULTS: The variety of medications and the most frequent combinations routinely used by patients with DS (AEDs and others) are described. Patients use a large number of pharmaceutical treatments to manage seizures. The five most commonly used AEDs were sodium valproate (66% of the patients; mean daily dose: 660 mg; 24.5 mg per kg bodyweight), bromide (44%; 1462 mg; 51.2 mg per kg), clobazam (41%; 10.4 mg; 0.32 mg per kg), stiripentol (35%; 797 mg; 27.6 mg per kg), and topiramate (24%; 107 mg; 3.5 mg per kg). Ninety percent had reported using emergency medications in the last 3 months;, with the most common medications being Buccolam (40%, an oromucosal form of midazolam) and diazepam (20%, mostly rectal application). No discernable relationships between current medication and age or seizure frequency were observed. SIGNIFICANCE: This is the first comprehensive report of routine AEDs and emergency medication use in a large sample of patients with DS in Germany over a period of 3-6 months and shows that despite the most common AED combinations being in line with clinical guidelines/best practice, there is no discernable impact of best treatment on seizure frequency. We find a higher use of bromide in Germany compared with other real-world evidence in Europe.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".