After sudden unexpected death in epilepsy: Lessons learned and the road forward
Bibliographic record
Abstract
The devastating effects of sudden unexpected death in epilepsy (SUDEP) can be difficult to navigate, even for experienced clinicians. Mounting evidence supports full disclosure of the risks of epilepsy to those affected and their caregivers, and recommendations from regulatory and professional groups encourage the same. Following a death, families are faced with tragedy, guilt, and sometimes anger. Clinicians are often called upon to provide information and support. The development of a comprehensive approach to SUDEP education requires careful consideration of the people living with epilepsy, facts about SUDEP and known risk factors, as well as experiences of families and care providers. In this article, we share the experiences of those working in SUDEP education and epilepsy care, including the voluntary sector. We explore the experience of bereaved families and clinicians, derive lessons from published research, highlight areas where more research is needed, and report on preliminary data from a nationwide study from France.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".