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Record W4409967934 · doi:10.1002/mdc3.70111

Tics and Parkinson's Disease: Clinical and Pathophysiological Insights from a Rare Syndromic Association

2025· article· en· W4409967934 on OpenAlexaff
Tarig Mohammed Abkur, Alexandra Boogers, Talyta Grippe, David A. Isaacs, Irene A. Malaty, Renato P. Munhoz, Kailash P. Bhatia, Lauren A. Hart, Alfonso Fasano, Suneil K. Kalia, Anthony E. Lang, Christos Ganos

Bibliographic record

VenueMovement Disorders Clinical Practice · 2025
Typearticle
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsOccupational Cancer Research CentreToronto Western HospitalKrembil FoundationUniversity of TorontoUniversity Health Network
FundersNational Institute of Neurological Disorders and Stroke
KeywordsTicsDeep brain stimulationDopaminergicNeuroscienceSubthalamic nucleusParkinson's diseaseParkinsonismPathophysiologyDegenerative diseasePsychologyDopamineDiseaseMedicineMovement disordersCentral nervous system diseaseInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The coexistence of tics with Parkinson's disease (PD) is rare, as they often emerge at different ages, follow different trajectories and involve contrasting pathophysiological mechanisms related to dopamine availability and function in the brain. CASES: We present 10 individuals with primary tic disorders who later developed PD. Tic severity remained unchanged with the onset of parkinsonism or dopaminergic treatment. Peak-dose dyskinesias in two cases did not affect tics, and deep brain stimulation of the subthalamic nucleus transiently induced tics in one individual with PD. CONCLUSIONS: The evidence drawn from this case series does not support a linear relation between nigrostriatal dopaminergic availability and tics. It suggests that tics may instead arise from a more complex interplay between multiple neurotransmitter systems acting on several networks within the cortico-striatal-thalamic circuits.

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)0.001
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.370
Teacher spread0.351 · 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 teacher head, not a consensus.

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

Citations3
Published2025
Admission routes1
Has abstractyes

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