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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".