Postictal brainstem hypoperfusion and risk factors for sudden unexpected death in epilepsy
Bibliographic record
Abstract
OBJECTIVE: Since the strongest risk factor for sudden unexpected death in epilepsy (SUDEP) is frequent bilateral tonic-clonic seizures (BTCS), our aim was to determine whether postictal hypoperfusion in brainstem respiratory centers (BRCs) is more common following tonic-clonic seizures. METHODS: We studied 21 patients with focal epilepsies who underwent perfusion imaging with arterial spin labeling MRI. Subtraction maps of cerebral blood flow were obtained from the postictal and baseline scans. We identified 6 regions of interest in the brainstem that contain key BRCs. Patients were considered to have postictal BRC hypoperfusion if any of the 6 regions of interest were significantly hypoperfused. RESULTS: All 6 patients who experienced BTCS during the study had significant clusters of postictal hypoperfusion in BRCs compared to 7 who had focal impaired awareness seizures (7/15). The association between seizure type studied and the presence of BRC hypoperfusion was significant. Duration of epilepsy and frequency of BTCS were not associated with postictal brainstem hypoperfusion despite also being associated with risk for SUDEP. CONCLUSION: Postictal hypoperfusion in brainstem respiratory centers occurs more often following BTCS than other seizure types, providing a possible explanation for the increased risk of SUDEP in patients who regularly experience BTCS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".