Dissecting the Effects of Disease and Treatment on Impulsivity in Parkinson's Disease
Bibliographic record
Abstract
Converging evidence, including observations in patients with Parkinson's disease (PD), suggests that dopamine plays a role in impulsivity. This multi-faceted construct includes considerations of both time and risk; determining how these more specific processes are affected by PD and dopaminergic treatment can inform neurobiological models. We examined the effects of PD and its treatment on temporal discounting and risky decision-making in a cohort of 23 mild-moderate PD patients and 20 healthy participants. Patients completed the Balloon Analogue Risk Task and a temporal discounting paradigm both on and off their usual dopamine replacement therapy. PD patients did not differ from controls in their initial risk-taking on the Balloon Analogue Risk Task, but took progressively more risks across trials when on medication. A subset of patients and controls was tested again, 1.5-3 years later, to explore the effects of disease progression. On follow-up, baseline risk-taking diminished in patients, but the tendency to take increasing risks across trials persisted. Neither disease progression nor its treatment affected the temporal discounting rate. These findings suggest a different neural basis for temporal discounting and risk-taking, and demonstrate that risk-taking can be further decomposed into initial and trial-by-trial effects, with dopamine affecting only the latter.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".