Sudden unexpected death in epilepsy: respiratory vs. cardiac contributions
Bibliographic record
Abstract
Sudden unexpected death in epilepsy (SUDEP) poses a significant risk to life expectancy for individuals with epilepsy. Mechanistic insight, while incomplete, has advanced through clinical observational studies and animal models. Yet we lack preventative therapies, which will depend on understanding SUDEP mechanisms. Recurrent convulsive seizures are the major SUDEP risk factor. Cardiorespiratory dysfunction precedes SUDEP, but whether cardiac arrhythmias are major proximate culprits for SUDEP remains to be determined. Here, we highlight recent data from mouse models and clinical studies that provide increasing support for respiratory depression and decreasing evidence for tachyarrhythmia-induced SUDEP. Further, we review data from genetic and chemoconvulsant mouse models that have enabled a deeper understanding for how seizures initiated in the central nervous system propagate to the autonomic nervous system and drive seizure-induced respiratory depression and subsequent SUDEP, rather than supporting a proximate cardiac arrhythmia cause. Ongoing research will continue to identify predictive SUDEP biomarkers, improve animal models, and translate basic research into precision medicine approaches. Identifying and understanding the brainstem circuits vulnerable in seizure-induced apnoea will enable therapeutic interventions to enhance the quality of life and life expectancy for individuals with epilepsy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".