Brief history of anti‐seizure drug development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The mainstay of therapy for epilepsy is anti-seizure drugs (ASDs, also referred to as anticonvulsants and anti-epileptic medications). Through much of the past century, only a handful for ASDs were available for clinical use. However, with the creation of the U.S. National Institutes of Health/National Institute of Neurological Disorders and Stroke (NINDS)-sponsored Anticonvulsant Screening Program (ASP), coupled with the emergence of high-throughput screening platforms and methodologies, and advances in our understanding of the fundamental neurobiology of epilepsy, ASD development has greatly accelerated over the past 25 years. More than 18 new ASDs have been approved for clinical use since the inception of the ASP. Despite this remarkable success and the emergence of drugs possessing more favorable pharmacokinetic profiles that act on novel molecular targets, there has been increasing recognition that the paradigms for drug discovery have not yielded significant improvements in therapeutic efficacy, and that disease modification (i.e., anti-epileptogenesis), among other challenges, must be addressed. Thus, with the renewed framework and mission of improving the lives of people with epilepsy, the name of the ASP was changed to the Epilepsy Therapy Screening Program (ETSP). This review briefly summarizes the history of ASD development and outlines some of the challenges and opportunities for the next generation of drug therapies for the epilepsy field.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it