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Record W1595118030 · doi:10.1002/mds.26068

Linking neuroscience with modern concepts of impulse control disorders in Parkinson's disease

2014· review· en· W1595118030 on OpenAlexaff
T. Celeste Napier, Jean‐Christophe Corvol, Anthony A. Grace, Jamie D. Roitman, James B. Rowe, Valerie Voon, Antonio P. Strafella

Bibliographic record

VenueMovement Disorders · 2014
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsToronto Western HospitalUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthNational Institute on Drug AbuseWellcome Trust
KeywordsNeuroscienceParkinson's diseaseNeuroimagingDiseasePsychologyImpulse controlImpulse control disorderDopamineMovement disordersMedicinePathology

Abstract

fetched live from OpenAlex

Patients with Parkinson's disease (PD) may experience impulse control disorders (ICDs) when on dopamine agonist therapy for their motor symptoms. In the last few years, a rapid growth of interest for the recognition of these aberrant behaviors and their neurobiological correlates has occurred. Recent advances in neuroimaging are helping to identify the neuroanatomical networks responsible for these ICDs, and together with psychopharmacological assessments are providing new insights into the brain status of impulsive behavior. The genetic associations that may be unique to ICDs in PD are also being identified. Complementing human studies, electrophysiological and biochemical studies in animal models are providing insights into neuropathological mechanisms associated with these disorders. New animal models of ICDs in PD patients are being implemented that should provide critical means to identify efficacious therapies for PD-related motor deficits while avoiding ICD side effects. Here, we provide an overview of these recent advances, with a particular emphasis on the neurobiological correlates reported in animal models and patients along with their genetic underpinnings.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.299
Teacher spread0.284 · 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 designOther design
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

Citations93
Published2014
Admission routes1
Has abstractyes

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