Review: The past, present and future challenges in epilepsy‐related and sudden deaths and biobanking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it