Research trends on interventional studies for Mild Cognitive Impairment (MCI): A bibliometric analysis using CiteSpace
Bibliographic record
Abstract
Abstract Background Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and the onset of dementia. The early detection of MCI is essential to avoid certain irreversible brain damage at the end of life. While there have been a variety of preventive interventions used in the past three decades, need to know the current trends is necessary for developing updated preventive modules for MCI. Objectives This bibliometric study examines the current trends in interventional research for treating MCI. Methods Published records were obtained from the Web of Science Core Collection (WoSCC) for the period of 1989–2023. CiteSpace 6.2.R4 (64-bit) advanced version software was utilized for mapping and bibliometric analysis of this study. Overall, 400 records were retrieved and analyzed using document co-citation analysis (DCA), author co-citation analysis (ACA), institutional, country based, and keyword analysis. Results It was found that the publication records were steadily increased in the recent five years (2018–2022) and shows that more than half of the interventional studies (234) were conducted on the recent times. Although United States (US) published highest number of publications (115), Canada secured top position based on burst (3.89) in country wise analysis. In ACA, Petersen RC_2003 secured top position based on citation counts (237), Rapp S_2006 on Burst strength (10.69), Ball K_2006 on Centrality (0.19) and Sigma value (2.00). Through the DCA analysis, clusters such as computerized cognitive training, virtual reality, rhythm training, and dance intervention, were considered to be the indicators of emerging trends.
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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 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.210 | 0.279 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| 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".