Trends and Insights in Cognitive Impairment and Depression Research: A Comprehensive Bibliometric Analysis
Bibliographic record
Abstract
INTRODUCTION: Cognitive impairment and depression are widespread, debilitating conditions that significantly impact quality of life and present major public health challenges. This study aimed to explore the intersection of cognitive impairment and depression, identifying trends, major contributors, influential studies, and emerging research subjects. METHODS: We conducted a bibliometric analysis using the Scopus database on July 21, 2024, covering the literature from 2000 to 2024. Data were extracted and analyzed using R (version 4.3.3) with the bibliometrix package and "biblioshiny" web interface for visualization. The analysis included assessing publication trends, identifying the leading authors, evaluating major journals, and tracking institutional contributions. Keyword co-occurrence and thematic tracking were used to explore the research focus subjects and evolving trends. RESULTS: Two thousand and fifty-one articles were identified, with annual scientific production showing a 7.71% growth rate, from 18 articles in 2000 to 195 articles in 2022. The average number of citations per article fluctuated, previously increasing but declining in recent years. The leading journals included the Journal of Affective Disorders and the American Journal of Geriatric Psychiatry . The leading authors were Zhang Y and Li Y, with significant contributions from the University of Toronto and the University of California. The USA led in article production, followed by China and Canada, with extensive international collaboration. The most cited document was that of Rock etal ., with 1336 citations. The keyword analysis highlighted "depression" as the most frequent term, and thematic tracking revealed distinct clusters of nonhuman and human research. DISCUSSION: This study's results inform future research guidelines and underscore the importance of interdisciplinary collaboration to address these interrelated conditions. It contributes to the existing literature by tracking the evolution and current state of research and guiding future studies toward emerging themes and gaps in this faculty.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.034 | 0.083 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".