Bibliographic record
Abstract
This publication offers a review of the pharmacological interventions studied for Parkinson's disease psychosis. Before initiating drug therapy for psychosis, possible contribution of antiparkinsonian medications to psychosis must be minimized by reducing their dose and completely switching them to levodopa if indicated. As a group, second- generation antipsychotics have been studied the most for Parkinson's disease psychosis. Evidence of efficacy for psychosis and safety for motor side effects is strongest for clozapine but routine use of clozapine is limited by its potential to cause agranulocytosis and the stringent monitoring requirements. Based on several open-label studies, quetiapine appeared to be a reasonable alternative, but recent double-blind studies create uncertainty about its efficacy. Olanzapine and aripiprazole have limited efficacy and are associated with worsening of motor symptoms. Literature is limited and lacks clarity about the utility of risperidone and ziprasidone in this scenario. Recently, encouraging literature has emerged on the use of donepezil and rivastigmine in patients with Parkinson's disease dementia and psychosis. There is a considerable need to further study the existing drugs and explore other pharmacotherapies in Parkinson's disease psychosis. Future research should ensure sound methodological quality and control for confounding variables to provide results that could be used reliably in clinical practice.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.005 | 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".