Review: The past, present and future challenges in epilepsy‐related and sudden deaths and biobanking
Bibliographic record
Abstract
Awareness and research on epilepsy-related deaths (ERD), in particular Sudden Unexpected Death in Epilepsy (SUDEP), have exponentially increased over the last two decades. Most publications have focused on guidelines that inform clinicians dealing with these deaths, educating patients, potential risk factors and mechanisms. There is a relative paucity of information available for pathologists who conduct these autopsies regarding appropriate post mortem practice and investigations. As we move from recognizing SUDEP as the most common form of ERD toward in-depth investigations into its causes and prevention, health professionals involved with these autopsies and post mortem procedure must remain fully informed. Systematizing a more comprehensive and consistent practice of examining these cases will facilitate (i) more precise determination of cause of death, (ii) identification of SUDEP for improved epidemiological surveillance (the first step for an intervention study), and (iii) biobanking and cell-based research. This article reviews how pathologists and healthcare professionals have approached ERD, current practices, logistical problems and areas to improve and harmonize. The main neuropathology, cardiac and genetic findings in SUDEP are outlined, providing a framework for best practices, integration of clinical, pathological and molecular genetic investigations in SUDEP, and ultimately prevention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".