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Record W2098354188 · doi:10.1002/mds.22898

Compulsive use of dopaminergic drug therapy in Parkinson's disease: Reward and anti‐reward

2010· article· en· W2098354188 on OpenAlexaff
Andrew Evans, Andrew D. Lawrence, Silke Appel‐Cresswell, Regina Katzenschlager, Andrew J. Lees

Bibliographic record

VenueMovement Disorders · 2010
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLevodopaDopaminergicPsychologyParkinson's diseaseDopamineAffect (linguistics)MoodPsychiatryMedicineDiseaseInternal medicineNeuroscience

Abstract

fetched live from OpenAlex

A few Parkinson patients develop a disabling pattern of compulsive dopaminergic drug use ("dopamine dysregulation syndrome"-DDS). DDS patients commonly identify aversive dysphoric "OFF" mood-states as a primary motivation to compulsively use their drugs. We compared motoric, affective, non-motor symptoms and incentive arousal after overnight medication withdrawal and after levodopa in DDS and control PD patients. Twenty DDS patients were matched to 20 control PD patients for age, gender, and disease duration and underwent a standard levodopa challenge. Somatic symptomatology, positive and negative affective states, drug effects, reward responsivity, motor disability, and dyskinesias were tested in the "OFF"-state after overnight withdrawal of medications, and then after a challenge with a standard dose of levodopa, after a full "ON"-state was achieved. In the "OFF"-state, DDS patients reported lower positive affect, and more motor and non-motor disability. In the "ON"-state, DDS patients had higher expressions of drug "wanting," reward responsivity, and dyskinesias. Positive and negative affect, non-motor symptomatology, and motor disability were comparable. These findings suggest that affective, motivational, and motoric disturbances in PD are associated with the transition to compulsive drug use in individuals who inappropriately overuse their dopaminergic medication.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.246
Teacher spread0.234 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations53
Published2010
Admission routes1
Has abstractyes

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