P.035 Cardiac screening in children with genetic epilepsy at risk for sudden unexpected death in epilepsy
Bibliographic record
Abstract
Background: People with epilepsy experience higher rates of cardiac arrhythmia and sudden death than the general population, with the highest risk in genetic epilepsies. Despite growing evidence of a possible cardiac contribution, routine cardiac screening for epilepsy patients is rarely performed. Methods: We performed a single center, retrospective review of patients with developmental epileptic encephalopathies caused by genetic variants expressed in the heart and brain. Clinical history, medications, age, and cardiac evaluation data were extracted. Results: Among 67 patients (56% female), 54 (81%) had at least one ECG. Twenty (37%) had an abnormal ECG. Forty-one had a repeat ECG: 8 showed persistent abnormalities, 7 resolution of abnormalities, and 7 a new abnormality. Five patients with an abnormality did not receive a follow up ECG. Two patients each had histories of cardiac arrest, syncope, and sudden death in a family member. Cardiac phenotypes differed in patients who experienced generalized tonic-clonic seizures and patients with epilepsy for 3+ years. Conclusions: Almost 1/3 of our high-risk epilepsy cohort had history of cardiac events or abnormalities on cardiac testing. Seizure type and epilepsy duration were associated with altered cardiac phenotypes. Since some findings were potentially clinically significant, routine cardiac screening of high-risk epilepsy patients may be warranted.
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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) | 0.005 | 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".