Bibliometric Analysis of the Research Status and Global Trends inBehavioral and Psychological Symptoms of Dementia in Alzheimer’sDisease from 2002 to 2022
Bibliographic record
Abstract
BACKGROUND: Several reviews on behavioral and psychological symptoms (BPSDs) in patients with Alzheimer's disease (AD) have summarized the current state of this field, but global trends are unclear. OBJECTIVE: This study utilized CiteSpace to provide a global overview of the current state of research on AD and its BPSDs and to predict future research trends in the field. METHODS: Data were retrieved from the Web of Science Core Collection. Bibliometric and cooccurrence analyses were performed using CiteSpace software. In total, 787 valid publications were included in the analysis. RESULTS: Publications on AD and BPSD have shown an increasing trend since 2002. The United States and the University of Toronto were the countries and institutions with the highest total number of publications, respectively. Japan and China were the second and third most influential in the field. Clive Ballard was the top author in terms of the number of publications. Journal of Alzheimer's Disease had the highest number of publications on this topic. Co-occurrence analysis showed that AD, behavioral symptoms, cognitive impairment, and early markers are hot topics in this area. Non-drug management of BPSDs, pharmacological treatment, and physiotherapy will be a hot topic in this field in the future. CONCLUSION: Our study visualized the relevant articles over the past 21 years to detect global hotspots and trends. Our findings may help researchers to identify research hotspots in this field and will help in the selection of appropriate research topics, while possibly leading to cross-regional cooperation.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.131 | 0.177 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".