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Record W2576230061 · doi:10.1111/epi.13649

De novo <i><scp>SCN</scp>1A</i> pathogenic variants in the <scp>GEFS</scp>+ spectrum: Not always a familial syndrome

2017· article· en· W2576230061 on OpenAlexaff
Kenneth A. Myers, Rosemary Burgess, Zaid Afawi, John A. Damiano, Samuel F. Berkovic, Michael S. Hildebrand, Ingrid E. Scheffer

Bibliographic record

VenueEpilepsia · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersNational Health and Medical Research Council
KeywordsGeneticsMedicineNeuroscienceBiology

Abstract

fetched live from OpenAlex

Genetic epilepsy with febrile seizures plus (GEFS+) is a familial epilepsy syndrome characterized by heterogeneous phenotypes ranging from mild disorders such as febrile seizures to epileptic encephalopathies (EEs) such as Dravet syndrome (DS). Although DS often occurs with de novo SCN1A pathogenic variants, milder GEFS+ spectrum phenotypes are associated with inherited pathogenic variants. We identified seven cases with non-EE GEFS+ phenotypes and de novo SCN1A pathogenic variants, including a monozygotic twin pair. Febrile seizures plus (FS+) occurred in six patients, five of whom had additional seizure types. The remaining case had childhood-onset temporal lobe epilepsy without known febrile seizures. Although early development was normal in all individuals, three later had learning difficulties, and the twin girls had language impairment and working memory deficits. All cases had SCN1A missense pathogenic variants that were not found in either parent. One pathogenic variant had been reported previously in a case of DS, and the remainder were novel. Our finding of de novo pathogenic variants in mild phenotypes within the GEFS+ spectrum shows that mild GEFS+ is not always inherited. SCN1A screening should be considered in patients with GEFS+ phenotypes because identification of pathogenic variants will influence antiepileptic therapy, and prognostic and genetic counseling.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.236
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations37
Published2017
Admission routes1
Has abstractyes

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