Sudden unexpected infant death, sudden unexplained death in childhood, and sudden unexpected death in epilepsy
Bibliographic record
Abstract
Sudden deaths in infants and children represent a profound and tragic event that continues to challenge researchers despite extensive investigation over several decades. The predominant phenotype, sudden infant death syndrome (SIDS), has evolved into the broader category of sudden unexpected infant death (SUID). In older children, a less understood phenomenon known as sudden unexplained death in childhood (SUDC) has garnered attention. Additionally, sudden unexpected death in epilepsy (SUDEP) constitutes a rare but recognized complication of epilepsy. Recent investigations indicate overlapping clinical, neuropathological, and genetic characteristics among SUID, SUDC, and SUDEP. Common features include death occurring during sleep, discovery in the prone position, hippocampal abnormalities, and genetic variations associated with epilepsy or cardiac arrhythmias. Notably, video recordings in certain examples of SUDC have captured 'convulsive' episodes preceding death in children without prior seizure history, suggesting that seizures may contribute more significantly to sudden paediatric deaths than previously presumed. This review explores these shared elements, underscoring their importance in formulating possible preventative measures against these devastating conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".