Dravet Syndrome: Addressing the Needs of Patients and Families: Introduction
Bibliographic record
Abstract
Abstract Dravet syndrome is not one of the most frequent severe epilepsies affecting infants during the first year of life. In the most recent epidemiological study, in Sweden, its estimated incidence was 1 in 33,000 live births. On December 31, 2011, its prevalence was 1 in 45,700 children aged less than 18 years. Nonetheless, it is now well known by many child neurologists for several reasons. First, its genetic aetiology was demonstrated almost 15 years ago, and an animal model was created shortly thereafter, allowing experimental work focused on the underlying mechanisms of the disease. Second, the clinical characteristics of the typical form of Dravet syndrome are well defined, enough to allow early diagnosis. Third, although the epileptic seizures are highly pharmacoresistant, we now have at our disposal a specific therapeutic strategy that allows one to avoid the most severe seizures in a number of patients due to the new drug stiripentol, used in different associations. Nevertheless, this therapeutic strategy should not be limited to seizure control and needs to take into account all other aspects of the disease. The aim of this symposium is to present a synthesis of the diagnosis and treatment of Dravet syndrome with a focus on family needs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".