Parkinson’s Disease Related Depression and Anxiety: A 22-Year Bibliometric Analysis (2000-2022)
Bibliographic record
Abstract
Tong Zhang,* Rui Yang,* Juhua Pan, Shijing Huang Research and Development Center of Traditional Chinese Medicine, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shijing Huang; Juhua Pan, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, People’s Republic of China, Email gamhsj@126.com; panjuhua@126.comBackground: Parkinson’s disease (PD) is one of the common neurodegenerative diseases. Depression and anxiety are the most common psychiatric symptoms of PD. It is important to study the potential relationship between PD and depression or anxiety.Aim: This study aimed to use bibliometrics to analyze the papers about parkinson’s disease related depression and anxiety over the last 22 years, and to characterize the current status of research and predict future hotspots.Methods: In the Web of Science Core Collection (WoSCC) from 2000 to 2022, documents are searched according to specific subject words. The selected literature was retrospectively analyzed and mapped using CiteSpace and Vosviewer software. We analyzed countries, institutions, journals, authors, references and keywords.Results: A total of 7368 papers were included from 2000 to 2022, and the number of publications has shown an upward trend year by year. Movement Disorder is the journal with the highest number of publications (391 publications, 5.31%) and citations (30,549 times), with the United States (2055 publications, 27.9%) and the University of Toronto (158 publications) being the countries and institutions with the highest number of publications. The high-frequency keywords focused on “quality of life”, “deep brain stimulation” and “non-motor symptoms”. “Functional connectivity”, “gut microbiota” and “inflammation” may be at the forefront of future research.Conclusion: Parkinson’s disease related depression and anxiety have been increasingly studied over the past 22 years. Functional connectivity, gut microbiota, and inflammation will be the subject of active research hotspots in the future, and these findings may provide new research ideas for researchers.Keywords: Parkinson’s disease, depression, anxiety, bibliometric analysis, Citespace, VOSviewer
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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: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review 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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.181 | 0.192 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".