Parahippocampal networks, intractability, and the chronic epilepsy of kindling.
Bibliographic record
Abstract
Clearly, the root cause of intractability in epilepsy is currently unknown. Whereas the aforementioned findings may shed light on putative underpinnings, they are by no means an exhaustive list of possibilities. However, new and more effective animal models are continually being created or discovered that take into account genetic predisposition for seizure. At the moment, amygdala kindling appears to be the best choice of the intact animal models. In this vein, the genetically predisposed seizure-prone (Fast kindling) and seizure-resistant (Slow kindling) strains may help speak to many important remaining questions in human epilepsy. Hopefully, these models, to some degree, target correct human subpopulations that are prone or resistant to epilepsy and, when used appropriately, could expedite epilepsy research and future discoveries leading to pharmacoresistance and intractability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".