Evidence-Based Review of Pharmacotherapy Used for Parkinson’s Disease Psychosis
Bibliographic record
Abstract
OBJECTIVE: To summarize and evaluate the existing literature regarding medications to treat Parkinson's disease (PD) psychosis. DATA SOURCES: MEDLINE (1946 to March 2017), EMBASE (1980 to March 2017), CINAHL (1982 to March 2017), and PsychInfo (1887 to March 2017) were searched using the following terms: Parkinson disease, Parkinson's disease, psychotic disorders, psychosis, delusions, and hallucinations. STUDY SELECTION AND DATA EXTRACTION: The search was limited to randomized controlled trials (RCTs) reporting human outcomes. Data extracted included the following: study design, population, setting, intervention, control, outcomes related to psychosis and safety, and potential biases assessed using Cochrane Collaboration's Risk of Bias Assessment Tool. DATA SYNTHESIS: After assessment, 16 of 235 studies were included; 11 articles reported comparisons between active drug and placebo, whereas 5 compared clozapine and an active comparator. Placebo-controlled trials demonstrated benefit for clozapine (n = 2) and pimavanserin (n = 2), with no firm benefits observed for quetiapine (n = 4) or olanzapine (n = 3). Comparative studies demonstrated improved efficacy in symptom scores when clozapine or comparator agent (n = 2, quetiapine; n = 1, olanzapine; n = 1, risperidone; and n = 1, ziprasidone) was assessed alone. However, no comparator data suggest that one agent is better than another, and none are yet available for pimavanserin. Overall risk of bias across all studies was moderate to high. CONCLUSIONS: Despite lack of rigor in study designs, published data to date suggest that clozapine and pimavanserin should be considered drugs of choice to treat PD psychosis.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".