MétaCan
Menu
Back to cohort
Record W4214853662 · doi:10.1177/08830738221081244

Further Characterization of <i>SMC1A</i> Loss of Function Epilepsy Distinct From Cornelia de Lange Syndrome

2022· article· en· W4214853662 on OpenAlexaff
Kristin Barañano, Amy Kimball, Susan L. Fong, Alena Egense, Catherine Hudon, Antonie D. Kline

Bibliographic record

VenueJournal of Child Neurology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Chromatin Dynamics
Canadian institutionsMcGill University
FundersCornelia de Lange Syndrome Foundation
KeywordsCornelia de Lange SyndromeEpilepsyEpilepsy syndromesBiologyGeneticsMedicinePsychiatry

Abstract

fetched live from OpenAlex

Cornelia de Lange syndrome is a rare developmental malformation syndrome characterized by small stature, limb anomalies, distinctive facial features, developmental delays, and behavioral issues. The diagnosis of Cornelia de Lange syndrome is made clinically or on the basis of an identified variant in one of the genes associated with Cornelia de Lange syndrome. SMC1A variants are the cause of 5% of the cases of Cornelia de Lange syndrome. SMC1A is located on the X-chromosome and is thought to escape X-inactivation in some females. Patients with SMC1A variants are being increasingly identified through panel testing or exome sequencing without prior clinical suspicion of Cornelia de Lange syndrome. In general, intractable epilepsy is not considered a prominent feature of Cornelia de Lange syndrome, yet this is found in these patients with SMC1A variants. Here we report on a series of patients with SMC1A variants and intractable epilepsy. In contrast to patients with typical SMC1A-associated Cornelia de Lange syndrome, all of the identified patients were female, and when available, X-inactivation studies were highly skewed with truncating variants. We describe the medical involvement and physical appearance of the participants, compared to the diagnostic criteria used for classical Cornelia de Lange syndrome. We also report on the clinical characteristics of the epilepsy, including age of onset, types of seizures, electroencephalographic (EEG) findings, and response to various antiepileptic medications. These findings allow us to draw conclusions about how this population of patients with SMC1A variants fit into the spectrum of Cornelia de Lange syndrome and the broader spectrum of cohesinopathies and allow generalizations that may impact clinical care and, in particular, epilepsy management.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.179
Teacher spread0.175 · 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 designCase report
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

Citations13
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Child NeurologySame topicGenomics and Chromatin DynamicsFrench-language works237,207