Bibliometric Analysis of Research Between Deep Brain Stimulation and Alzheimer’s Disease
Bibliographic record
Abstract
Objective: To examine the thematic evolution and scientific productivity of the relationship between deep brain stimulation (DBS) and Alzheimer’s disease (AD). Materials and methods: A descriptive study using a scientometric approach was conducted using the Scopus database between 2019 and July 2024. Data were exported to the SciVal bibliometric analysis tool, and bibliometric indicators, such as number of publications per country/region, citations per publication, and h -index, were used. Results: A total of 150 publications were obtained, mostly of high quality (Q1) and coming from the USA and China. Lozano was the most productive author, and the University of Toronto in Canada had the highest number of publications. The journal with the highest impact was Frontiers in Neuroscience with 21.3 citations/paper, and most authors only published one article. Conclusions: The bibliometric study revealed decreasing research trends but mostly highlighted large international collaborations, especially in high-impact journals. The findings can serve as a basis for future research and policies evaluating the impact of DBS in the management of patients with AD.
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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 | 0.017 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.181 | 0.249 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".