Clinical characteristics and treatment experience of individuals with SCN8A developmental and epileptic encephalopathy (SCN8A-DEE): Findings from an online caregiver survey
Bibliographic record
Abstract
PURPOSE: SCN8A developmental epileptic encephalopathy (SCN8A-DEE) is a rare and severe genetic epilepsy syndrome characterized by early-onset developmental delay, cognitive impairment, and intractable seizures. SCN8A gene variants are associated with a broad phenotypic spectrum and variable disease severity. A caregiver survey, solicited by the advocacy group The Cute Syndrome Foundation (TCSF), was conducted to gather information on the demographics/disease presentation, seizure history, and treatment of patients with SCN8A-related epilepsies. METHODS: A 36-question online survey was developed to obtain de-identified data from caregivers of children with SCN8A-related epilepsy. The survey included questions on genetic diagnosis, disease manifestations/comorbidities, seizure severity/type, current/prior use of antiseizure medicines (ASMs), and best/worst treatments per caregiver perception. RESULTS: In total, 116 survey responses (87 USA, 12 Canada, 12 UK, 5 Australia) were quantitatively analyzed. Generalized tonic/clonic was the most common seizure type at onset and time of survey; absence and partial/focal seizures were also common. Most patients (77%) were currently taking ≥2 ASMs; 50% had previously tried and stopped ≥4 ASMs. Sodium channel blockers (oxcarbazepine, phenytoin, lamotrigine) provided the best subjective seizure control and quality of life. CONCLUSION: The SCN8A-DEE patient population is heterogeneous in seizure characteristics and ASMs taken and is difficult to treat, with high seizure burden and multiple comorbidities. The high proportion of patients who previously tried and stopped ASMs indicates large unmet treatment need. Further collaboration between families, caregivers, patient advocates, clinicians, researchers, and industry can increase awareness and understanding of SCN8A-related epilepsies, improve clinical trial design, and potentially improve patient outcomes.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".