Bibliometrics and visual analysis of metformin and gut microbiota from 2012 to 2022: A systematic review
Bibliographic record
Abstract
BACKGROUND: Metformin is an old drug used for the treatment of type 2 diabetes mellitus and can play a variety of roles by regulating the gut microbiota. The number of research articles on metformin in the gut microbiota has increased annually; however, no bibliometric tools have been used to analyze the research status and hot trends in this field. This study presents a bibliometric analysis of publications on metformin and gut microbiota. METHODS: We searched the Web of Science core collection database on June 8, 2023, for papers related to metformin and gut microbiota from 2012 to 2022. We used Microsoft Excel 2021, VOSviewer1.6.19, CiteSpace 6.2.4, and R software package "bibliometrix" 4.0.0 to analyze the countries, institutions, authors, journals, citations, and keywords of the included publications. RESULTS: We included 517 papers, and the trend in publications increased over the last 11 years. The 517 articles were from 57 countries, including 991 institutions and 3316 authors, and were published in 259 journals. China led all countries (233 papers) and the most influential institution was the Chinese Academy of Sciences (16 papers). PLOS ONE (19 papers) was the most popular journal, and Nature (1598 citations) was the most cited journal. Li and Kim were the 2 most published authors (six papers each), and Cani (272 co-citations) was the most co-cited author. "Metabolites," "aging," and "intestinal barrier" were emerging topics in this field. CONCLUSIONS: This bibliometric study comprehensively summarizes the research trends and progress of metformin and gut microbiota, and provides new research topics and trends for studying the effects of metformin on gut microbiota in different diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.014 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.117 | 0.125 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".