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Record W2900012975 · doi:10.1002/mdc3.12615

Quick Flicks: Association of Paroxysmal Kinesigenic Dyskinesia and Tics

2018· article· en· W2900012975 on OpenAlexaff
Bettina Balint, Sarah Wiethoff, Davide Martino, Claudia Del Gamba, Anna Latorre, Christos Ganos, Henry Houlden, Kailash P. Bhatia

Bibliographic record

VenueMovement Disorders Clinical Practice · 2018
Typearticle
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsUniversity of Calgary
FundersEuropean Social FundAllerganDeutsche ForschungsgemeinschaftNational Institute for Health and Care ResearchRobert Bosch StiftungWellcome TrustMedical Research CouncilWellcome
KeywordsParoxysmal dyskinesiaTicsDyskinesiaAssociation (psychology)MedicineDystoniaPsychologyNeuroscienceInternal medicinePsychotherapistParkinson's disease

Abstract

fetched live from OpenAlex

BACKGROUND: Paroxysmal kinesigenic dyskinesia (PKD) is a rare disorder characterised by brief attacks of chorea, dystonia, or mixed forms precipitated by sudden movement. METHODS: mutations. RESULTS: In a series of 14 PKD patients seen in our clinic at the National Hospital of Neurology, Queen Square, from 2012-2017, we noted tics in 11 patients (79%), which stand in stark contrast to the estimated lifetime prevalence of tics estimated to reach 1%. CONCLUSIONS: The two reasons to point out this possible association are the clinical implications and the potential opportunity of a better understanding of shared pathophysiological mechanisms of neuronal hyperexcitability.

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)0.002
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.021
GPT teacher head0.382
Teacher spread0.361 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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