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Record W4405552873 · doi:10.1002/mdc3.14306

Diagnostic Utility of Clinical Neurophysiology in Jerky Movement Disorders: A Review from the <scp>MDS</scp> Clinical Neurophysiology Study Group

2024· review· en· W4405552873 on OpenAlexafffund
Anna Latorre, Christos Ganos, Masashi Hamada, Nicolás Phielipp, Lorenzo Rocchi, Shabbir Hussain I. Merchant, Marina A.J. Tijssen, Sterre van der Veen, Robert Chen

Bibliographic record

VenueMovement Disorders Clinical Practice · 2024
Typereview
Languageen
FieldMedicine
TopicGlycogen Storage Diseases and Myoclonus
Canadian institutionsUniversity Health NetworkToronto Western HospitalUniversity of Toronto
FundersEngineering and Physical Sciences Research CouncilCanadian Institutes of Health ResearchUniversity of TorontoVolkswagen FoundationZonMwNederlandse Organisatie voor Wetenschappelijk OnderzoekIpsenUniversity Health Network FoundationEuropean Regional Development FundEuropean CommissionParkinson's FoundationNational Organization for Rare DisordersMedical Research CouncilInternational Parkinson and Movement Disorder SocietyTeva Pharmaceutical Industries
KeywordsMyoclonusNeurophysiologyClinical neurophysiologyMovement disordersNeuroscienceTicsDystoniaPsychologyElectromyographyMedicineMyokymiaPhysical medicine and rehabilitationElectroencephalographyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Myoclonus and other jerky movement disorders are hyperkinetic disorders, the diagnosis of which heavily relies on clinical neurophysiological testing. However, formal diagnostic criteria are lacking, and recently the utility and reliability of these tests have been questioned. OBJECTIVE: The aim of this review was to assess the utilization of clinical neurophysiology testing to identify possible gaps and boundaries that might guide the development of new methods for a more precise diagnosis and in-depth understanding of myoclonus. METHODS: We reviewed electrophysiological features of cortical myoclonus, subcortical myoclonus (ie, myoclonus associated with dystonia, brainstem myoclonus), excessive startle reflex, spinal myoclonus (ie, spinal segmental and propriospinal myoclonus), peripheral myoclonus and mimics of myoclonus of peripheral origin (hemifacial spasm, minipolymyoclonus, myokymia), functional jerky movements, chorea, and tics. RESULTS: Electrophysiological features that support the recognition of myoclonus subtypes, such as muscle burst duration, muscle pattern of activation, measures of cortical excitability, or movement-related cortical potentials, have been identified. These significantly contribute to the diagnosis of jerky movement disorders, but their reliability is uncertain. Despite the significant advancements, several unresolved questions persist. Factors contributing to this include the absence of systematic neurophysiological assessment and standardized methods, alongside the limited number of patients investigated using these techniques. CONCLUSION: Although clinical neurophysiology remains the "gold standard" for defining and diagnosing myoclonus, our review highlighted the need to enhance the quality and reliability of neurophysiological testing in jerky movement disorders. Further studies including larger cohorts of patients recruited from different centers, employing standardized and optimized electrophysiological techniques, are warranted.

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)0.009
metaresearch head score (Gemma)0.130
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.864
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.130
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.006
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0010.007
Insufficient payload (model declined to judge)0.0000.001

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 head0.114
GPT teacher head0.477
Teacher spread0.363 · 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; both teacher heads agree on what is shown here.

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

Citations6
Published2024
Admission routes2
Has abstractyes

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