Knowledge mapping and visualization of current sarcopenia and cancer research: a bibliometric analysis
Bibliographic record
Abstract
Abstract Background Cancer survivors face broad challenges in weight loss due to multiple factors. Sarcopenia prevalence among cancer survivors has a wide range and is associated with worse outcomes. Sarcopenia and cancer attract global attention. The use of bibliometrics analysis in this area of interest still needs to be identified. This study was performed to assess the global trends and patterns of sarcopenia and cancer-related scientific publications. Methods Web of Science (WOS) and articles indexed in Science Citation Index Expanded. VOS viewer (Leiden University, Leiden, Netherlands) and R-studio using bibliometrics and R package were used for quantitative analysis of the dataset (year of publications, number of publications, institutes, journals, total citations, H-index status, authors, hotspots of institutes, Keywords, research area, and funding sponsor. Results Our analysis extracted 384 publications from 172 journals written by 2525 authors from the Institute for Web of Science Core Collection database. Overall, 384 articles from the WOS database met the inclusion criteria. The number of published papers has risen since 2014. The results showed that Japan, China and the USA contributed the most to this field. Moreover, our results recognized future research trends and the current condition of sarcopenia and cancer research based on the top 10 most cited articles and the keyword analysis. Finally, the leading author's analysis demonstrated that Shen Xian from Wenzhou Medical University, China, Baracos and Vickie E from the University of Alberta, Switzerland, were the most productive, active, and influential authors. Conclusion Our study demonstrates a comprehensive and objective overview of the up-to-date status of sarcopenia and cancer research. These data would benefit scholars who need information on sarcopenia and cancer research. It would be a reference guide for researchers wanting to conduct additional studies related to the topic.
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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 | 0.012 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.144 | 0.152 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".