Diagnostic Utility of Exome Data Reanalysis After <i>In Silico</i> Multi‐Gene Panels or Clinical Exome Testing for Patients With Epilepsy and Developmental Delay/Intellectual Disability: A Retrospective Cohort Study
Bibliographic record
Abstract
Epilepsy is a relatively common condition with genetic factors contributing significantly to its etiology. Advances in next-generation sequencing have dramatically increased the number of known epilepsy genes, improving diagnostic capabilities and patient care. However, 50%-80% of epilepsy patients remain undiagnosed after genomic testing, which includes chromosomal microarray, multigene panels, and genome-wide sequencing. Reanalysis of existing exome sequencing data has shown promise in increasing diagnostic yield. In this study, we reanalyzed exome sequencing data from 87 individuals with unsolved epilepsy and developmental delay or intellectual disability in Ontario, Canada. Our approach combined clinical and translational research methodologies to identify genetic variants linked to epilepsy. We obtained a diagnostic yield of 14.9%, solving 13 participants, with 11 involving known genes and two novel gene discoveries. In addition, 11 potential diagnoses were identified, suggesting that further investigation could confirm additional diagnoses. Factors such as the inclusion of additional family data, new disease-gene associations, and technological advancements contributed to these findings. This study highlights the importance of reanalysis as a cost-effective and timely approach to improving diagnostic yield in epilepsy associated with neurodevelopmental delay.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".