A Bibliometric Analysis of the Global Research on Multimorbidity in Older Adults from 2000 to 2023
Bibliographic record
Abstract
ObjectiveTo analyze the research trends and cutting-edge hot spots in the field of multimorbidity in older adults from 2000 to 2023 to provide reference for related research.MethodsWe conducted a search in the Web of Science Core Collection database, specifically looking for articles or reviews in English on multimorbidity in older adults published between January 1, 2000 and October 24, 2023. VOSviewer 1.6.18 software was used to extract the contents in the literature and draw the cooperative network diagram of high-producing countries(≥30 articles) and institutions(≥43 articles) as well as the timeline diagram of high-frequency keywords(≥74 occurrences) co-occurrence relationship. CiteSpace 6.1.R6 software was used to co-occur and cluster analyze the information of authors, research institutions and countries. The "bibliometrix" package in R was used to analyze the evolution of keywords in the literature.ResultsA total of 2590 documents consisting of 2230 Articles and 360 Reviews were obtained. The worldwide publication count significantly increased since 2000. Among the countries, the United States had the highest number of publications (35.02%, 907/2590) and total citations(31 343 times) in this field. The University of Toronto in Canada had the largest number of articles(2.59%, 67/2590). Jenny Ploeg of McMaster University was recognized as the most prolific author(1.24%, 32/2590). When it came to journals, BMC Geriatrics had the highest amount of literature related to multimorbidity(3.82%, 99/2590). The key areas of research in this field included multimorbidity, older adults, frailty, aging, and polypharmacy. Notably, there was growing interest in studying the relationship between multimorbidity and aging in older adults, as well as the impact of frailty and polypharmacy on multimorbidities.ConclusionsIn recent years, research on multimorbidities in the elderly has primarily centered around examining the correlation between comorbidities and aging, as well as exploring the impact of frailty and polypharmacy on individuals with multimorbidities. Future research could delve into primary health care, comprehensive geriatric assessment for older adults with multimorbidities, and maintenance of their overall quality of life.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.148 | 0.182 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".