Investigating the Genetic Causes of Sudden Unexpected Death in Children Through Targeted Next-Generation Sequencing Analysis
Bibliographic record
Abstract
BACKGROUND: Inherited arrhythmia syndromes are responsible for a significant portion of autopsy-negative sudden unexpected death (SUD) cases, but molecular autopsy used to identify potentially causal variants is not routinely included in SUD investigations. We collaborated with a medical examiner's office to assist in finding a diagnosis for their autopsy-negative child SUD cases. METHODS AND RESULTS: 191 child SUD cases (<5 years of age) were selected for analyses. Our next generation sequencing panel incorporated 38 inherited arrhythmia syndrome candidate genes and another 33 genes not previously investigated for variants that may underlie SUDY pathophysiology. Overall, we identified 11 potentially causal disease-associated variants in 12 cases, for an overall yield of 6.3%. We also identified 31 variants of uncertain significance in 36 cases and 16 novel variants predicted to be pathogenic in silico in 15 cases. The disease-associated variants were reported to the medical examiner to notify surviving relatives and recommend clinical assessment. CONCLUSIONS: We have identified variants that may assist in the diagnosis of at least 6.3% of autopsy-negative child SUD cases and reduce risk of future SUD in surviving relatives. We recommend a cautious approach to variant interpretation. We also suggest inclusion of cardiomyopathy genes as well as other candidate SUD genes in molecular autopsy analyses.
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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 | 0.001 | 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) | 0.001 | 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".