Abstract 11616: Seizures in Infants with Congenital Heart Disease
Bibliographic record
Abstract
Background: Infants with congenital heart disease (CHD) may have an increased risk to develop seizures during hospitalization. Objectives: to examine the association of CHD with non-epileptic, non-traumatic seizures in hospitalized infants up to one year of age. Methods: We conducted a retrospective analysis of the U.S. National Inpatient Sample dataset (2015-2018). We used the international classification of disease-10 th version to identify infants with different types of CHD. We included infants up to one year of age. We excluded infants <35 week gestational age, infants with central nervous system or with major congenital anomalies, common trisomy or genetic disorders. We used chi square testing to examine the association of subtypes of CHD with seizures excluding post traumatic and epileptic seizures. We calculated mortality in infants with CHD who developed seizures versus those who did not have seizures. Results: There was 12,023,781 infants in the weighted sample who met our inclusion criteria. Of them, 48.7% were females, 46.7% were Caucasians, 195,685 (1.63%) were identified with a CHD and 20,565 (0.17%) suffered at least one episode of seizure during hospitalization. Seizures occurred in 4225 (2.2%) of infants with any type of CHD compared to 16,340 (0.14%) in infants with no CHD, odds ratios for seizures is 15.6 (CI: 15.2-16.5, p <0.001). The association of seizures with subtypes of CHD is shown in table-1. Among infants with CHD, 2205 infants expired during hospitalization. Mortality was higher in infants with CHD who suffered seizures (8.5%) vs. those who did not have seizures (1.0%), OR 9.6 (8.5-10.8, p <0.001). Conclusion: Infants with CHD have a higher chance to experience seizure episodes during their hospitalization and those who have seizures have higher chances of mortality during hospitalization. Further analysis is required to understand the pathological correlation between seizures and CHD and its impact on long term developmental outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".