109 Examining the impact of a genetic etiology on clinical outcomes in children living with paediatric epilepsy
Bibliographic record
Abstract
Abstract Background Paediatric epilepsy poses a substantial clinical challenge, particularly when standard treatments fall short of achieving optimal control. Traditional approaches to antiseizure medication (ASM) selection often involve trial and error, which can lead to extended periods of inadequate management and increased risk of adverse effects. Genetic testing has emerged as a promising tool for guiding ASM selection by identifying epilepsy-related genetic variations, allowing for a more personalized approach. Preliminary studies suggest that genetic results can help optimize ASM choice; however, research is still limited in directly assessing the impact on clinical outcomes, such as long-term seizure control and quality of life improvements. Further investigation is needed to evaluate how genetic insights translate into tangible benefits for paediatric epilepsy patients. Objectives This study aims to compare seizure outcomes between paediatric epilepsy patients with a genetic etiology and those with a non-genetic etiology, using a sample of New Brunswick children who have undergone genetic testing. Positive genetic test results can lead to tailored treatment plans, while negative results typically receive standard treatment. By evaluating outcomes in children receiving personalized management based on genetic findings versus those receiving conventional treatment, the study seeks to determine if genetic etiology is associated with improved seizure outcomes in paediatric epilepsy. Design/Methods This longitudinal study utilized quantitative methods to assess seizure outcomes in paediatric epilepsy patients at three time points: baseline (6 months pre-genetic test), 6 months post-genetic test, and 12 months post-genetic test. The study included New Brunswick patients aged 0-18 who underwent genetic testing. A sample of 35 patients was selected using proportionate random sampling, comprising 21 with positive genetic test results and 14 with negative results. Seizure outcomes were measured for frequency, severity, duration, and freedom at each time point. Data was extracted from deidentified patient charts, and genetic test results were interpreted independently to minimize bias. Statistical analysis included descriptive statistics for demographic variables and outcomes, followed by Friedman Tests to assess within-group changes over time. Pairwise Wilcoxon Signed-Ranks Tests evaluated specific time point differences, while Cochran’s Q tests assessed seizure freedom outcomes, all at a significance level of α = 0.05. Results For patients with positive genetic test results, significant reductions in seizure frequency, severity, and duration were observed from baseline to 12 months, indicating enhanced outcomes with targeted ASM treatment. Specifically, seizure frequency decreased significantly between baseline and 12 months (p = 0.0005), and 90.5% of the positive test group achieved seizure freedom within 12 months. In contrast, the negative test group showed a slower improvement trajectory, with only 64.3% achieving seizure freedom by 12 months. Moreover, improvements in seizure outcomes for the positive test group occurred earlier, with reductions in severity evident by 6 months. The study supports that a positive genetic test can potentially enable faster, more effective treatment in paediatric epilepsy by guiding ASM selection and mitigating adverse effects associated with standard treatments. Conclusion This study suggests that in paediatric epilepsy positive genetic test results may facilitate more effective ASM selection, earlier reductions in seizure severity, and potentially higher rates of seizure freedom, underscoring the potential for improved patient outcomes through personalized management. However, as an initial study, these findings highlight the need for further research to fully understand the long-term impacts of tailored management and the broader applicability of genetic testing in diverse paediatric epilepsy populations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".