Bibliographic record
Abstract
Epilepsy is commonly encountered in forensic pathology and is ultimately determined to be the cause of death in 1–2% of medicolegal death investigations. Epilepsy is a risk factor for death from external causes, including accidents and drowning. More commonly, deaths result from the underlying epilepsy pathology, including intracranial neoplasms, cerebrovascular disease, status epilepticus, and sudden unexpected death in epilepsy (SUDEP). SUDEP refers to the sudden death in an epilepsy patient that lacks an alternative anatomic or toxicological cause of death. At autopsy, intracranial pathology is present in the majority of epilepsy-related deaths and is more likely to be identified following brain fixation. Common findings include brain tumors, mesial temporal sclerosis, and malformations of cortical development. Death investigators should pay particular attention to clinical history to establish a clear history of epilepsy and to determine seizure type, frequency, underlying etiology, and prior medical and surgical treatments as well as other comorbid medical conditions. A complete autopsy with toxicology is necessary to identify other causes of death, particularly in cases of suspected SUDEP. While toxicology may be helpful in some cases, caution must be taken in interpreting postmortem antiepileptic drug concentrations as levels decrease postmortem.
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.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".