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Record W3139195718 · doi:10.3389/fnins.2021.654238

Impulse Control Disorders in Parkinson’s Disease: From Bench to Bedside

2021· review· en· W3139195718 on OpenAlexafffund
Andrea Augustine, Catharine A. Winstanley, Vaishnav Krishnan

Bibliographic record

VenueFrontiers in Neuroscience · 2021
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchRice University
KeywordsParkinson's diseaseNeurochemicalNeuroscienceImpulse controlDopamineImpulse control disorderDiseaseMedicineDopamine agonistAgonistBench to bedsidePsychologyDopaminergicInternal medicinePathologicalReceptor

Abstract

fetched live from OpenAlex

Parkinson's disease (PD) is a neurodegenerative disorder that is characterized by symptoms that impact both motor and non-motor domains. Outside of motor impairments, PD patients are at risk for impulse control disorders (ICDs), which include excessively disabling impulsive and compulsive behaviors. ICD symptoms in PD (PD + ICD) can be broadly conceptualized as a synergistic interaction between dopamine agonist therapy and the many molecular and circuit-level changes intrinsic to PD. Aside from discontinuing dopamine agonist treatment, there remains a lack of consensus on how to best address ICD symptoms in PD. In this review, we explore recent advances in the molecular and neuroanatomical mechanisms underlying ICD symptoms in PD by summarizing a rapidly accumulating body of clinical and preclinical studies, with a special focus on the utility of rodent models in gaining new insights into the neurochemical basis of PD + ICD. We also discuss the relevance of these findings to the broader problem of impulsive and compulsive behaviors that impact a range of neuropsychiatric syndromes.

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)0.001
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.020
GPT teacher head0.304
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 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

Citations24
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in NeuroscienceSame topicParkinson's Disease Mechanisms and TreatmentsFrench-language works237,207