WONOEP appraisal: Modeling early onset epilepsies
Bibliographic record
Abstract
Epilepsy has a peak incidence during the neonatal to early childhood period. These early onset epilepsies may be severe conditions frequently associated with comorbidities such as developmental deficits and intellectual disability and, in a significant percentage of patients, may be medication-resistant. The use of adult rodent models in the exploration of mechanisms and treatments for early life epilepsies is challenging, as it ignores significant age-specific developmental differences. More recently, models developed in immature animals, such as rodent pups, or in three-dimensional organoids may more closely model aspects of the immature brain and could result in more translatable findings. Although models are not perfect, they may offer a more controlled screening platform in studies of mechanisms and treatments, which cannot be done in pediatric patient cohorts. On the other hand, more simplified models with higher throughput capacities are required to deal with the large number of epilepsy candidate genes and the need for new treatment options. Therefore, a combination of different modeling approaches will be beneficial in addressing the unmet needs of pediatric epilepsy patients. In this review, we summarize the discussions on this topic that occurred during the XVI Workshop on Neurobiology of Epilepsy, organized in 2022 by the Neurobiology Commission of the International League Against Epilepsy. We provide an overview of selected models of early onset epilepsies, discussing their advantages and disadvantages. Heterologous expression models provide initial functional insights, and zebrafish, rodent models, and brain organoids present increasingly complex platforms for modeling and validating epilepsy-related phenomena. Together, these models offer valuable insights into early onset epilepsies and accelerate hypothesis generation and therapy discovery.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.010 |
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".