Research on depression in Parkinson disease: A bibliometric and visual analysis of studies published during 2012–2021
Bibliographic record
Abstract
BACKGROUND: The diagnosis and treatment rate of Parkinson disease (PD) with depression has a low diagnostic rate, and there is no consensus on the choice of treatment mode. This study evaluates the global research trends of scientific outputs related to depression in PD from multiple perspectives, using a bibliometric analysis and visualization tool to scientifically analyze the knowledge from the literature. METHODS: Literature related to depression in PD published from 2012 to 2021 was included and selected from the Web of Science Core Collection database in October 2021. CiteSpace software was used to visualize and analyze co-occurrence analyses for countries, institutions, authors, and keywords. RESULTS: A total of 4533 articles from the Web of Science database were included. The United States made the largest contribution with the majority of publications (1215; 29.40%). Toronto University was the most productive institution. PD, depression, quality of life, dementia, nonmotor symptom, prevalence, anxiety, Alzheimer disease, symptom, and disorder would be significantly correlated with depression in PD. The current hot spots in this field focus on the following: risk factors for depression in PD, assessment scale of depression in PD, and rehabilitation of depression in PD. CONCLUSIONS: This analysis not only reveals the current research trends and hotspots but also provides some instructive suggestions on the development of depression in PD.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.219 | 0.211 |
| 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, 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".