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Record W3015941631 · doi:10.1002/mus.26887

Guidelines on clinical presentation and management of nondystrophic myotonias

2020· review· en· W3015941631 on OpenAlexfundno aff
Bas C. Stunnenberg, Samantha LoRusso, W. David Arnold, Richard J. Barohn, Stephen C. Cannon, Bertrand Fontaine, Robert C. Griggs, Michael G. Hanna, Emma Matthews, G. Meola, Valeria Sansone, Jaya Trivedi, Baziel G.M. van Engelen, Savine Vicart, Jeffrey Statland

Bibliographic record

VenueMuscle & Nerve · 2020
Typereview
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesWellcome TrustAdvanced Foods and Materials Canada
KeywordsMexiletineMyotoniaMyotonia congenitaMedicineChannelopathyWeaknessMuscle relaxationMuscle weaknessAnesthesiaInternal medicineSurgeryMyotonic dystrophy

Abstract

fetched live from OpenAlex

The nondystrophic myotonias are rare muscle hyperexcitability disorders caused by gain-of-function mutations in the SCN4A gene or loss-of-function mutations in the CLCN1 gene. Clinically, they are characterized by myotonia, defined as delayed muscle relaxation after voluntary contraction, which leads to symptoms of muscle stiffness, pain, fatigue, and weakness. Diagnosis is based on history and examination findings, the presence of electrical myotonia on electromyography, and genetic confirmation. In the absence of genetic confirmation, the diagnosis is supported by detailed electrophysiological testing, exclusion of other related disorders, and analysis of a variant of uncertain significance if present. Symptomatic treatment with a sodium channel blocker, such as mexiletine, is usually the first step in management, as well as educating patients about potential anesthetic complications.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.012

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 head0.317
GPT teacher head0.461
Teacher spread0.144 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations95
Published2020
Admission routes1
Has abstractyes

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