Dead in the water: Epilepsy‐related drowning or <scp>sudden unexpected death in epilepsy</scp>?
Bibliographic record
Abstract
OBJECTIVE: Both drowning and sudden unexpected death in epilepsy (SUDEP) are diagnoses of exclusion with predominantly nonspecific autopsy findings. We hypothesized that people with epilepsy found dead in water with no clear sign of submersion could be misdiagnosed as SUDEP. METHODS: All reported seizure-related deaths undergoing medicolegal investigation in three medical examiner's offices (New York City, Maryland, San Diego County) over different time periods were reviewed to identify epilepsy-related drownings and SUDEPs. Drowning cases that fulfilled inclusion criteria were divided into two groups according to the circumstances of death: definite drowning and possible drowning. The SUDEP group included two sex- and age (±2 years)-matched definite SUDEP/definite SUDEP plus cases for each drowning case. RESULTS: Of 1346 deaths reviewed, we identified 36 definite (76.6%) and 11 possible drowning deaths (23.4%), most of which occurred in a bathtub (72.3%). There were drowning-related findings, including fluid within the sphenoid sinuses, foam in the airways, clear fluid in the stomach content, and lung hyperinflation in 58.3% (21/36) of the definite drowning group, 45.5% (5/11) of the possible drowning group, and 4.3% of the SUDEP group (4/92). There was no difference in the presence of pulmonary edema/congestion between the definite drowning group, possible drowning group, and SUDEP group. The definite drowning group had a higher mean combined lung weight than the SUDEP group, but there was no difference in mean lung weights between the possible drowning and SUDEP groups or between the possible drowning and definite drowning groups. SIGNIFICANCE: No distinguishable autopsy finding could be found between SUDEPs and epilepsy-related drownings when there were no drowning-related signs and no clear evidence of submersion. SUDEP could be the cause of death in such possible drowning cases. As most drowning cases occurred in the bathtub, supervision and specific bathing precautions could be effective prevention strategies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".