Progress and prospects of Parkinson’s disease with depression research: A global bibliometric analysis based on CiteSpace
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disorder marked by motor impairments such as stiffness, involuntary shaking, and slowed movement. In addition, PD patients frequently experience nonmotor symptoms, especially depression. This study uses a mixed-methods scientometric analysis to review global research trends and advancements in PD and depression. This analysis is vital for clinicians, researchers, and policymakers, identifying knowledge gaps and directing future research efforts. METHODS: We conducted a comprehensive literature review on PD and depression using the Web of Science database from 2004 to 2023, facilitated by CiteSpace 6.1.R6. Our analysis examined collaborations among authors, institutions, countries, and keywords, incorporating insights from RCTs and qualitative studies. We calculated effect sizes and confidence intervals with precision. Ethical approval was not required as the study used publicly available data without personal information. RESULTS: Our analysis included 3048 research papers and 915 reviews, involving 17,927 authors and 12,466 institutions. The United States and the University of Toronto led in publications. Studies revealed significant effect sizes with narrow confidence intervals, particularly on the prevalence and impact of depression in PD patients. High-frequency keywords included "Parkinson's disease," "depression," "quality of life," "non-motor symptom," and "dementia." Visual mapping identified critical research nodes and future directions. CONCLUSION: Over the past 2 decades, research on the PD-depression link has accelerated. Our analysis highlights prevailing trends and critical areas, providing evidence-based recommendations for therapeutic strategies. This study offers valuable insights for clinicians and researchers, emphasizing future research priorities to improve patient outcomes.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.031 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.229 | 0.310 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".