Sudden Death After Febrile Seizure Case Report: Cerebral Suppression Precedes Severe Bradycardia
Bibliographic record
Abstract
A 20-month-old girl with a complex chromosomal disorder had first presentation of febrile status epilepticus and was admitted to the hospital. Two days after her initial seizure, she died suddenly and unexpectedly during a video EEG monitoring study. An advanced analysis of the physiologic changes in the hours and minutes leading up to death was undertaken. The electrocardiography over the last 19 minutes of life was reviewed, and the R-R intervals were manually measured. Heart rate variability was assessed through calculation of the SD of the R-R intervals and the root mean square of successive differences over successive 100 beat periods. Instantaneous heart rate, SD of the R-R intervals, the root mean square of successive differences, and oxygen saturation were plotted against time over the last 19 minutes of life. Diffuse cerebral suppression on EEG was observed 10 minutes before death, followed minutes later by severe bradycardia and increased heart rate variability. Although the child did not meet criteria for a diagnosis of epilepsy, the sequence of physiologic changes leading up to death suggests a pathophysiology similar to sudden unexplained death in epilepsy. A comparable pattern of diffuse cerebral suppression preceding parasympathetic overactivity has been suggested in some rare cases of adults who have experienced sudden unexplained death in epilepsy during video EEG monitoring.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".