Which terms should be used to describe medications used in the treatment of seizure disorders? An <scp>ILAE</scp> position paper
Bibliographic record
Abstract
A variety of terms, such as "antiepileptic," "anticonvulsant," and "antiseizure" have been historically applied to medications for the treatment of seizure disorders. Terminology is important because using terms that do not accurately reflect the action of specific treatments may result in a misunderstanding of their effects and inappropriate use. The present International League Against Epilepsy (ILAE) position paper used a Delphi approach to develop recommendations on English-language terminology applicable to pharmacological agents currently approved for treating seizure disorders. There was consensus that these medications should be collectively named "antiseizure medications". This term accurately reflects their primarily symptomatic effect against seizures and reduces the possibility of health care practitioners, patients, or caregivers having undue expectations or an incorrect understanding of the real action of these medications. The term "antiseizure" to describe these agents does not exclude the possibility of beneficial effects on the course of the disease and comorbidities that result from the downstream effects of seizures, whenever these beneficial effects can be explained solely by the suppression of seizure activity. It is acknowledged that other treatments, mostly under development, can exert direct favorable actions on the underlying disease or its progression, by having "antiepileptogenic" or "disease-modifying" effects. A more-refined terminology to describe precisely these actions needs to be developed.
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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 | 0.025 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.013 |
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".