Incorporating epilepsy genetics into clinical practice: a 360°evaluation
Bibliographic record
Abstract
Abstract We evaluated a new epilepsy genetic diagnostic and counseling service covering a UK population of 3.5 million. We calculated diagnostic yield, estimated clinical impact, and surveyed referring clinicians and families. We costed alternative investigational pathways for neonatal onset epilepsy. Patients with epilepsy of unknown aetiology onset < 2 years; treatment resistant epilepsy; or familial epilepsy were referred for counseling and testing. We developed NGS panels, performing clinical interpretation with a multidisciplinary team. We held an educational workshop for paediatricians and nurses. We sent questionnaires to referring paediatricians and families. We analysed investigation costs for 16 neonatal epilepsy patients. Of 96 patients, a genetic diagnosis was made in 34% of patients with seizure onset < 2 years, and 4% > 2 years, with turnaround time of 21 days. Pathogenic variants were seen in SCN8A, SCN2A, SCN1A, KCNQ2, HNRNPU, GRIN2A, SYNGAP1, STXBP1, STX1B, CDKL5, CHRNA4, PCDH19 and PIGT. Clinician prediction was poor. Clinicians and families rated the service highly. In neonates, the cost of investigations could be reduced from £9362 to £2838 by performing gene panel earlier and the median diagnostic delay of 3.43 years reduced to 21 days. Panel testing for epilepsy has a high yield among children with onset < 2 years, and an appreciable clinical and financial impact. Parallel gene testing supersedes single gene testing in most early onset cases that do not show a clear genotype-phenotype correlation. Clinical interpretation of laboratory results, and in-depth discussion of implications for patients and their families, necessitate multidisciplinary input and skilled genetic counseling.
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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 | 0.037 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".