Bibliometric Analysis of Alzheimer's Disease and Depression
Bibliographic record
Abstract
BACKGROUND: The link between Alzheimer's disease and depression has been confirmed by clinical and epidemiological research. Therefore, our study examined the literary landscape and prevalent themes in depression-related research works on Alzheimer's disease through bibliometric analysis. METHODS: Relevant literature was identified from the Web of Science core collection. Bibliometric parameters were extracted, and the major contributors were defined in terms of countries, institutions, authors, and articles using Microsoft Excel 2019 and VOSviewer. VOSviewer and CiteSpace were employed to visualize the scientific networks and seminal topics. RESULTS: The analysis of literature utilised 10,553 articles published from 1991 until 2023. The three countries or regions with the most publications were spread across the United States, China, and England. The University of Toronto and the University of Pittsburgh were the major contributors to the institutions. Lyketsos, Constantine G., Cummings, JL were found to make outstanding contributions. Journal of Alzheimer's Disease was identified as the most productive journal. Furthermore, "Alzheimer's", "depression", "dementia", and "mild cognitive decline" were the main topics of discussion during this period. LIMITATIONS: Data were searched from a single database to become compatible with VOSviewer and CiteSpace, leading to a selection bias. Manuscripts in English were considered, leading to a language bias. CONCLUSION: Articles on "Alzheimer's" and "depression" displayed an upward trend. The prevalent themes addressed were the mechanisms of depression-associated Alzheimer's disease, the identification of depression and cognitive decline in the early stages of Alzheimer's, alleviating depression and improving life quality in Alzheimer's patients and their caregivers, and diagnosing and treating neuropsychiatric symptoms in Alzheimer. Future research on these hot topics would promote understanding in this field.
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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 | 0.011 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.163 | 0.200 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".