Global research trends and hotspots in Parkinson’s disease psychosis: a 25-year bibliometric and visual analysis
Bibliographic record
Abstract
Background Parkinson’s disease psychosis (PDP) is one of the most severe and disabling non-motor symptoms in the progression of Parkinson’s disease (PD), significantly impacting the prognosis of PD patients. In recent years, there has been an increase in literature on PDP. However, bibliometrics has rarely been applied to PDP research. This study provides an overview of the current state of PDP research and predicts future trends in this field. Methods The literature search was conducted using the Web of Science Core Collection, with the search terms (Parkinson* AND (psychotic* OR hallucination* OR illusion* OR delusion* OR misperception* OR psychosis OR psychoses)). VOSviewer and CiteSpace software were employed to perform bibliometric analysis and visual representation of the search results. Results A total of 603 articles were effectively included. Since 2017, there has been a significant upward trend in publications related to PDP. The United States, the United Kingdom, and Canada were the top three contributing countries in terms of publication volume, with France also having a strong influence in this field. Movement Disorders and King’s College London included and published the most articles on PDP. The paper titled “Hallucinations in Parkinson’s Disease: Prevalence, Phenomenology, and Risk Factors” received the highest number of citations and average citations. Cluster analysis results identified brain, prevalence, connectivity, and atypical antipsychotics as key hotspots in this field. High-frequency keywords were grouped into three themes: neurobiology, therapeutic strategies, and symptom research. Among them, pimavanserin, risk, and functional connectivity have been the most studied areas in the past 7 years and are likely to remain key topics in future research. Conclusion Research on PDP has garnered increasing attention. This study visualizes PDP research over the past 25 years to analyze global hotspots and trends. It offers researchers a valuable perspective for identifying key topics and understanding research trajectories in this expanding field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.275 | 0.287 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".