Parkinson's Disease Treatment: A Bibliometric Analysis
Bibliographic record
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder marked by motor symptoms like bradykinesia, tremor, rigidity, and postural instability. Patients also experience non-motor symptoms that greatly affect their quality of life. The global prevalence of PD is increasing, especially among the elderly, necessitating effective treatment strategies. This review provides an overview of the current treatment modalities for PD, including pharmacological and surgical interventions, and employs a bibliometric analysis to evaluate the trends and impact of scientific research in this field. A comprehensive search of the Web of Science Core Collection (WoSCC) database was conducted on July 12, 2024, yielding 3,724 publications related to PD treatment. Bibliometric analysis was performed using Biblioshiny and VOSviewer to assess publication trends, impact, and collaborative networks. Metrics such as the number of publications, citations, h-index, and country/institutional contributions were analyzed to identify key areas of focus and influential research in PD treatment. The analysis revealed a significant increase in PD research output from 2000 onwards, peaking between 2011 and 2016. The United States led in research production, followed by China, Canada, and the United Kingdom. Key researchers included Lang AE, Okun MS, and Lozano AM, with the University of Toronto, University of California System, and Harvard University being the top contributing institutions. The study identified major trends in pharmacological treatments, such as dopamine replacement therapy and deep brain stimulation (DBS) as the most common surgical intervention. Bibliometric analysis highlighted significant international collaborations and identified influential studies shaping the current understanding and treatment of PD. This bibliometric analysis elucidated the trends and impacts of scientific contributions, emphasizing the prolific output from leading countries and institutions in relation to the treatment of Parkinson's disease. Take-home messages for the conclusion of our study are as follows: (1) this study found a substantial increase in Parkinson's disease (PD) research output from 2000 onwards, peaking around 2017-2018, (2) noted a decline in publication output post-2020, (3) the United States had the highest research output, followed by significant contributions from countries like China, Canada, and the United Kingdom, (4) international collaborations played a vital role in advancing PD research, (5) key researchers in the field were Lang AE, Okun MS, and Lozano AM, (6) and established institutions like the University of Toronto, Johns Hopkins University and Harvard University made substantial contributions to the field, emphasizing the role of leading academic centers in driving PD research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.035 | 0.129 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".