Compulsive use of dopaminergic drug therapy in Parkinson's disease: Reward and anti‐reward
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".