P.059 Exome-based testing for seizure indications captures a broader range of diagnostic genes and more diagnostic variants than provincially-funded epilepsy panels
Bibliographic record
Abstract
Background: Ontario and other Canadian provinces fund multi-gene sequencing panels as the initial testing approach for patients with epilepsy. However, genetic testing guidelines issued by the US-based National Society for Genetic Counselors and endorsed by the American Epilepsy Society recommend exome as a first-line test. We explored the theoretical improvements in diagnostic yield when selecting exome over provincially-funded panels (PFPs). Methods: Our comparative analysis used a list of 768 diagnostic genes and 4474 diagnostic variants identified in diagnostic exome cases involving clinical indications of seizure. We compared these lists to the genes included in two PFPs (190 genes and 474 genes) to see which exome-identified genes and variants would have been captured by the PFPs. Results: Most exome-identified diagnostic genes may have been missed by the PFPs (82% and 65% for the 190 and 474-gene PFPs), and close to half of the exome-identified diagnostic variants (62% and 43% for the 190 and 474-gene PFPs) may have been missed. Conclusions: Exome-based testing captures a broader range of diagnostic genes and more diagnostic variants than PFPs. The adoption of exome over panels as a first-line test may lead to improved diagnostic rates and permit earlier treatment for individuals with seizures.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.002 |
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".