Effect of Antidepressants on Psychotic Symptoms in Parkinson Disease: A Review of Case Reports and Case Series
Bibliographic record
Abstract
OBJECTIVES: The treatment of Parkinson disease (PD) psychosis remains a challenge. Only a few treatments eliciting significant relief of psychotic symptoms have passed the test of randomized controlled trials. METHODS: Here, we conducted a review of the literature on the effect of antidepressants on PD psychosis. Because there is no randomized controlled trial that assessed the antipsychotic effects of antidepressants in PD, only case reports, case series, and open-label trials were available to review. Because of the scarce literature, statistical analysis could not be performed. RESULTS: The following antidepressants alleviated hallucinations in PD: amoxapine, citalopram, clomipramine, escitalopram, mianserin, mirtazapine, and venlafaxine. The antidepressants were generally well tolerated, with the exception of amoxapine, which exacerbated parkinsonism. CONCLUSIONS: Whereas the conclusions that can be drawn on the efficacy of antidepressants at reducing PD psychosis are limited because of the poor quality of the reported studies, it is encouraging to notice that there are positive anecdotal reports. Further studies are needed to confirm the potential of these drugs and also to determine if a subtype of patients or of psychotic features may be more likely to be improved by antidepressants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".