Spectrum of impulse control behaviours in Parkinson’s disease: pathophysiology and management
Bibliographic record
Abstract
Impulse control behaviours (ICBs) are a range of behaviours linked by their reward-based, repetitive natures. They can be precipitated in Parkinson's disease (PD) by dopamine replacement therapy, often with detrimental consequences for patients and caregivers. While now a well-recognised non-motor feature of treated PD, much remains unknown about the influence of risk factors, pathophysiological mechanisms, vulnerability factors for specific types of behaviour and the optimal management strategies. Imaging studies have identified structural and functional changes in striatal and prefrontal brain regions, among others. Gene association studies indicate a role for genetic predisposition to PD-ICB. Clinical observational studies have identified potential modifiable and non-modifiable risk factors. Psychological studies shed light on the neurocognitive domains implicated in PD-ICBs and identify psychosocial determinants that may perpetuate the cycle of impulsive and harm-avoidance behaviours. Based on these results, a range of pharmacological and non-pharmacological management strategies have been trialled in PD-ICBs with varying success. The purpose of this review is to update clinicians on the evidence around the pathophysiology of PD-ICB. We aim to translate our findings into an interpretable biopsychosocial model that can be applied to the clinical assessment and management of individual cases of PD-ICB.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".