Publication trends in nutrition research for sarcopenic obesity: A 20-year bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: We used bibliometric methods to evaluate publications on the role of nutrition in sarcopenic obesity and analyzed the current situation and developmental trends over the past 2 decades. METHODS: Publications from 2002 to 2022 related to the role of nutrition in sarcopenic obesity were extracted from the Web of Science Core Collection database. CiteSpace, VOSviewer, and the Bibliometrix R package were applied to build relevant network diagrams. RESULTS: One thousand ninety-four articles from 64 countries were included. The annual number of publications in this field has shown an intense growth trend. The University of Alberta, Yonsei University, and Korea University are the major research institutions. Clinical Nutrition has published the most papers on the role of nutrition in sarcopenic obesity, and the American Journal of Clinical Nutrition is the most co-cited journal. A total of 5834 authors conducted the relevant studies. Yves Boirie has published the most papers in this field, and AJ Cruz-Jentoft is the most co-cited author. CONCLUSION: This is the first bibliometric study of the role of nutrition in sarcopenic obesity. This study systematically summarizes the research hotspots and development directions in this field, and provides a reference for scholars studying the role of nutrition in sarcopenic obesity.
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
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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.185 | 0.212 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".