Knowledge mapping of prodromal Parkinson’s disease: A bibliometric review and analysis (2000–2023)
Bibliographic record
Abstract
The prodromal period of Parkinson's disease (PD) is currently a hot topic in PD research. However, no bibliometric analysis has been conducted in this research field. This study aimed to provide a comprehensive overview of the status, hotspots, and trends in the prodromal period of PD using bibliometrics. CiteSpace and visualization of similarities viewer were used to analyze articles and reviews on the prodromal period of PD in the Web of Science Core Collection (WoSCC) database. We analyzed the data on countries, institutions, journals, authors, keywords, and cited references. In total, 909 articles from 65 countries, including the United States (n = 265, 29.15%) and Germany (n = 174, 19.14%), were included. The number of articles and reviews related to the prodromal period of PD has increased yearly. The University of Tubingen (n = 45, 4.95%), McGill University (n = 33, 3.63%), and University of London (n = 33, 3.63%) were the research institutions with the most published studies. Movement Disorders is the journal with the largest number of published papers (n = 98, 10.8%) and the most cited publications (co-citation = 7035). These publications are from 4681 authors, with Berg (n = 49, 5.39%) and Postuma (n = 40, 4.40%) publishing the most publications, and Postuma's study (n = 1206) having the most citations. Studying the nonmotor symptoms of PD precursors is a major topic in this research field. This is the first bibliometric study to comprehensively summarize the research trends and developments in the prodromal period of PD. This information identifies recent research frontiers and hotspots and provides a reference for scholars studying the prodromal period of 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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.157 | 0.182 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".