Global trends in sarcopenia and cancer over the past 10 years: a bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: Sarcopenia is common among patients with cancer. The alterations in the internal milieu of cancer patients, coupled with the adverse effects of antineoplastic therapies, markedly augment the susceptibility to sarcopenia. We aimed to clarify the current research status and investigate future trends in sarcopenia and cancer research. METHODS: Publications on sarcopenia and cancer from the past decade were retrieved from the Web of Science database. VOSviewer, CiteSpace, and Bibliometrix R package were used for visualization analysis. RESULTS: A total of 3749 publications were retrieved between 2014 and 2023. These publications were written by 21,507 authors affiliated with 4068 organizations in 76 countries/regions. Japan, the United States, and China constituted the primary contributors to the majority of the publications. The top three research institutions with the highest outputs in this field were the University of Alberta, Wenzhou Medical University, and Maastricht University. The Journal of Cachexia Sarcopenia and Muscle served as the pivotal and most cited journal in this field. Baracos VE from the University of Alberta was the author with the most publications. "Sarcopenic obesity", "Radiomics", and "Neutrophil/lymphocyte ratio" were highly focused topics in current research. CONCLUSION: This study conducted the first bibliometric analysis of literature on sarcopenia and cancer. A systematic analysis of the present research status and emerging trends in this field provides important references for future research.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.127 | 0.166 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".