Evaluation of development‐specific targets for antiepileptogenic therapy using rapid kindling
Bibliographic record
Abstract
We used the method of rapid hippocampal kindling to assess the potential antiepileptogenic efficacy of a number of anticonvulsant medications. This method afforded a higher throughput than methods based on traditional kindling or post-status epilepticus models of epileptogenesis. This "compressed epileptogenesis" model also permitted the study of age-dependent pharmacologic targets, and distinguished among antiepileptic drugs (AEDs) on the basis of their age-specific antiepileptogenic efficacy. We found retigabine to be the most effective anticonvulsant therapy during early development. Topiramate seemed most effective further along development, whereas some drugs did not demonstrate an age-specific effect. The method also reproduced some of the paradoxical pharmacologic findings previously shown with lamotrigine. Although the utility of this model for screening the antiepileptogenic therapies requires further validation, it introduces the ability to undertake development-specific testing and a more rapid throughput than conventional methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".