Bibliographic record
Abstract
Dravet syndrome is among the most challenging electroclinical syndromes. There is a high likelihood of recurrent status epilepticus; seizures are medically refractory; and patients have multiple co-morbidities, including intellectual disability, behaviour and sleep problems, and crouch gait. Additionally, they are at significant risk of sudden unexplained death. This review will focus predominantly on the prophylactic medical management of seizures, addressing both first-line therapies (valproate and clobazam) as well as second-line (stiripentol, topiramate, ketogenic diet) or later options (levetiracetam, bromides, vagus nerve stimulation). Sodium channel agents-including carbamazepine, oxcarbazepine, phenytoin and lamotrigine-should be avoided, as they typically exacerbate seizures. Several agents in development may show promise, specifically fenfluramine and cannabidiol, but they need further evaluation in randomized, controlled trials. In addition to prophylactic treatment, all patients need home-rescue medication and a status epilepticus protocol that can be carried out in their local hospital. Families must be counselled on non-pharmacologic strategies to reduce seizure risk, including avoidance of triggers that commonly induce seizures (including hyperthermia, flashing lights and patterns). In addition to addressing seizures, holistic care for a patient with Dravet syndrome must involve a multidisciplinary team that includes specialists in physical, occupational and speech therapy, neuropsychology, social work and physical medicine.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".