Considerations for determining the efficacy of new antiseizure medications in children age 1 month to younger than 2 years
Bibliographic record
Abstract
OBJECTIVES: Drug treatment for children with epilepsy should, ideally, be governed by evidence from adequate and well-controlled clinical studies. However, these studies are difficult to conduct, and so direct evidence supporting the informed use of specific drugs is often lacking. The Research Roundtable for Epilepsy (RRE) met in 2020 to align on an approach to therapy development for focal seizures in children age 1 month <2 years of age. METHODS: The RRE reviewed the regulatory landscape, epidemiology, seizure semiology, antiseizure medicine pharmacology, and safety issues applicable to this population. RESULTS: After reviewing evidence, the conclusion was that pediatric efficacy trials would be impracticable to conduct but a waiver of the regulatory requirement to conduct any study would lead to an absence of information to guide dosing in a critical population. Review of available data and discussion of RRE attendees led to the conclusion that the requirements for extrapolation of efficacy from older children down to infants from age 1 month to <2 years old appeared to be met. After the RRE, the US Food and Drug Administration (FDA) approved brivaracetam for use in children with focal epilepsy above the age of 1 month in August 2021 and lacosamide in October 2021, both based on the principle of extrapolation from data in older children. SIGNIFICANCE: These recommendations should result in more rapid accessibility of antiseizure medications for infants.
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 | 0.061 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| 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 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".