Bibliographic record
Abstract
Premature mortality among epilepsy patients is well recognized. Except a few identifiable causes of unnatural death, more than half of the epilepsy related death remains unexplained after extensive workup. These cases are classified as sudden unexpected death in epilepsy (SUDEP). SUDEP incidence varies significantly depending on the population, the methods documenting cause of death and the availability of Neuropathological examination. An accurate diagnosis of the cause of death is needed for epilepsy related death. The goal of this study is to present the relevant clinical data, the general autopsy and Neuropathology findings of epilepsy related death investigated in London Health Sciences Center during the period of 2000 to 2011. We identified 71 cases with known history of chronic epilepsy. In the 29 cases of epilepsy associated death, the causes of death have been classified as cardiac, pulmonary, accidental (e.g. drowning), toxic (e.g. drug overdose) and non-related causes. Forty two cases are considered to be SUDEP, and were categorized according to the recently proposed SUDEP Definition and Classification. Half of the SUDEP cases have no specific Neuropathological findings. The most common identifiable lesions in SUDEP cases are perinatal/neonatal destructive lesions (29%), hippocampal sclerosis (24%), and focal cortical dysplasia (20%). These are followed by neuronal heterotopia (9%), previous head trauma (9%), and cavernoma (5%). LEARNING OBJECTIVES This presentation will enable the learner to: 1. Review cause of death in epilepsy related deaths 2. Discuss the practice guideline in neuropathology autopsy of epilepsy related deaths
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".