Seizures in Mouse Models of Rare Neurodevelopmental Disorders
Bibliographic record
Abstract
Genetic neurodevelopmental disorders - that often include epilepsy as part of their phenotype - are a heterogeneous and clinically challenging spectrum of disorders in children. Although seizures often contribute significantly to morbidity in these affected populations, the mechanisms of epileptogenesis in these conditions remain poorly understood. Different model systems have been developed to aid in unraveling these mechanisms, which include a number of specific mutant mouse lines which genocopy specific general types of mutations present in patients. These mouse models have not only allowed for assessments of behavioral and electrographic seizure phenotypes to be ascertained, but also have allowed effects on the neurodevelopmental alterations and cognitive impairments associated with these disorders to be examined. In addition, these models play a role in advancing our understanding of these epileptic processes and developing preclinical therapeutics. The concordance of seizure phenotypes - in a select group of rare, genetic, neurodevelopmental disorders and epileptic encephalopathies - found between human patients and their model counterparts will be summarized. This review aims to assess whether models of Rett syndrome, CDKL5 deficiency disorder, Fragile-X syndrome, Dravet syndrome, and Ohtahara syndrome phenocopy the seizures seen in human patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".