Ketogenic diet treatment increases longevity in <i>Kcna1</i>‐null mice, a model of sudden unexpected death in epilepsy
Bibliographic record
Abstract
Individuals with poorly controlled epilepsy have a higher risk for sudden unexpected death in epilepsy (SUDEP). With approximately one third of people with epilepsy not achieving adequate seizure control with current antiseizure drugs, there is a critical need to identify treatments that reduce risk factors for SUDEP. The Kcna1-null mutant mouse models risk factors and terminal events associated with SUDEP. In the current study, we demonstrate the progressive nature of epilepsy and sudden death in this model (mean age of mortality (± SEM), postnatal day [P] 42.8 ± 1.3) and tested the hypothesis that long-term treatment with the ketogenic diet (KD) will prolong the life of Kcna1-null mice. We found that the KD postpones disease progression by delaying the onset of severe seizures and increases the lifespan of these mutant mice by 47%. Future studies are needed to determine the mechanisms underlying the KD effects on longevity.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".