Development of new treatment approaches for epilepsy: Unmet needs and opportunities
Bibliographic record
Abstract
A working group was created to address clinical "gaps to care" as well as opportunities for development of new treatment approaches for epilepsy. The working group primarily comprised clinicians, trialists, and pharmacologists. The group identified a need for better animal models for both efficacy and tolerability, and noted that animal models for potential disease-modifying or antiepileptogenic effect should mirror conditions in human trials. For antiseizure drugs (ASDs), current animal models have not been validated with respect to their relationship to efficacy in common epilepsy syndromes. The group performed an "expert opinion" survey of perceived efficacy of the available ASDs, and identified a specific unmet need for ASDs to treat tonic-atonic and myoclonic seizures. No correlation has as yet been demonstrated between animal models of tolerability and adverse effects (AEs), versus tolerability in humans. There is a clear opportunity for improved therapies in relation to dose-related AEs. The group identified common and rare epilepsy syndromes that could represent opportunities for clinical trials. They identified opportunities for antiepileptogenic (AEG) therapies in both adults and children, acknowledging that the presence of a biomarker would substantially improve the chances of a successful trial. However, the group acknowledged that disease-modifying therapies (given after the first seizure or after the development of epilepsy) would be easier to study than AEG therapies.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".