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Record W1973951229 · doi:10.1111/epi.12168

<i>SCN1A</i> testing for epilepsy: Application in clinical practice

2013· article· en· W1973951229 on OpenAlexaff
Shinichi Hirose, Ingrid E. Scheffer, Carla Marini, Peter De Jonghe, Eva Andermann, Alica M. Goldman, Marcelo Kauffman, Nigel CK Tan, Daniel H. Lowenstein, Sanjay M. Sisodiya, Ruth Ottman, Samuel F. Berkovic

Bibliographic record

VenueEpilepsia · 2013
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsDravet syndromeStatus epilepticusGenetic testingEpilepsyMedicinePediatricsEpilepsy syndromesVigabatrinNeuroscienceBioinformaticsPsychologyPsychiatryAnticonvulsantInternal medicineBiology

Abstract

fetched live from OpenAlex

This report is a practical reference guide for genetic testing of SCN1A, the gene encoding the α1 subunit of neuronal voltage-gated sodium channels (protein name: Nav 1.1). Mutations in this gene are frequently found in Dravet syndrome (DS), and are sometimes found in genetic epilepsy with febrile seizures plus (GEFS+), migrating partial seizures of infancy (MPSI), other infantile epileptic encephalopathies, and rarely in infantile spasms. Recommendations for testing: (1) Testing is particularly useful for people with suspected DS and sometimes in other early onset infantile epileptic encephalopathies such as MPSI because genetic confirmation of the clinical diagnosis may allow optimization of antiepileptic therapy with the potential to improve seizure control and developmental outcome. In addition, a molecular diagnosis may prevent the need for unnecessary investigations, as well as inform genetic counseling. (2) SCN1A testing should be considered in people with possible DS where the typical initial presentation is of a developmentally normal infant presenting with recurrent, febrile or afebrile prolonged, hemiclonic seizures or generalized status epilepticus. After age 2, the clinical diagnosis of DS becomes more obvious, with the classical evolution of other seizure types and developmental slowing. (3) In contrast to DS, the clinical utility of SCN1A testing for GEFS+ remains questionable. (4) The test is not recommended for children with phenotypes that are not clearly associated with SCN1A mutations such as those characterized by abnormal development or neurologic deficits apparent at birth or structural abnormalities of the brain. Interpreting test results: (1) Mutational testing of SCN1A involves both conventional DNA sequencing of the coding regions and analyses to detect genomic rearrangements within the relevant chromosomal region: 2q24. Interpretation of the test results must always be done in the context of the electroclinical syndrome and often requires the assistance of a medical geneticist, since many genomic variations are possible and it is essential to differentiate benign polymorphisms from pathogenic mutations. (2) Missense variants may have no apparent effect on the phenotype (benign polymorphisms) or may represent mutations underlying DS, MPSI, GEFS+, and related syndromes and can provide a challenge in interpretation. (3) Conventional methods do not detect variations in introns or promoter or regulatory regions; therefore, a negative test does not exclude a pathogenic role of SCN1A in a specific phenotype. (4) It is important to note that a negative test does not rule out the clinical diagnosis of DS or other conditions because genes other than SCN1A may be involved. Obtaining written informed consent and genetic counseling should be considered prior to molecular testing, depending on the clinical situation and local regulations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.038
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0300.035

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 head0.058
GPT teacher head0.401
Teacher spread0.343 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations73
Published2013
Admission routes1
Has abstractyes

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