Bibliographic record
Abstract
In the past twenty years, thirteen new antiepileptic drugs (AEDs) have been introduced, each differing in their efficacy spectrum, mechanism of action, pharmacokinetics, safety and tolerability profiles. These newer AEDs symbolize a welcoming future in the management of epilepsy because they are able to produce a remarkable reduction in seizure frequency in up to 40% to 50% of the patients who had been refractory to old generation drugs. Despite the current availability of these new drugs, only a few patients with truly refractory seizures can be made seizure free. Although the newer agents are not superior to that of the older drugs, some have been shown to be non-inferior in terms of their efficacy. They offer additional advantages like better tolerability, ease of use, reduced interaction profile. Even though in most situations the older generation drugs still represent the best choice, advancing studies show that in many conditions, new generation drugs may be entirely vindicated for initial therapy. This urges a need for the search of novel and more efficacious new antiepileptic drugs in the management of uncontrollable seizures. More direct comparisons of newer versus newer and newer versus older drugs in clinical trials, both for monotherapy and adjunctive therapy must be conducted. More than 20 compounds with promising antiepileptic and neuroprotective properties have been discovered and are under various stages of drug development. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".