Usage of Genetic Panels in an Adult Epilepsy Clinic
Bibliographic record
Abstract
ABSTRACT: Background: There is limited data on the utility, yield, and cost efficiency of genetic testing in adults with epilepsy. We aimed to describe the yield and utility of genetic panels in our adult epilepsy clinic. Methods: We performed a retrospective, cross-sectional study of all patients followed by an epileptologist at a Canadian tertiary care centre’s epilepsy clinic between January 2016 and August 2021 for whom a genetic panel was ordered. A panel was generally ordered when the etiology was unknown or in the presence of a malformation of cortical development. We determined the yield of panel positivity and of confirmed genetic diagnoses. We also estimated the proportion of these diagnoses that were clinically actionable. Results: In total, 164 panels were ordered in 164 patients. Most had refractory epilepsy (80%), and few had comorbid intellectual disability (10%) or a positive family history of epilepsy (11%). The yield of panel positivity was 11%. Panel results were uncertain 49% of the time and negative 40% of the time. Genetic diagnoses were confirmed in 7 (4.3%) patients. These genetic conditions involved the following genes: SCARB2, DEPDC5, PCDH19, LGI1, SCN1A, MT-TL1, and CHRNA7. Of the seven genetic diagnoses, 5 (71%) were evaluated to be clinically actionable. Conclusion: We report a lower diagnostic yield for genetic panels in adults with epilepsy than what has so far been reported. Although the field of the genetics of epilepsy is a fast-moving one and more data is required, our findings suggest that guidelines for genetic testing in adults are 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 | 0.001 | 0.009 |
| 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) | 0.002 | 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".