Mapping the Muscle Mass: A Birds-Eye View of Sarcopenia Research Through Bibliometric Network Analysis
Bibliographic record
Abstract
Sarcopenia, characterized by progressive age-associated loss of skeletal muscle mass and function, has emerged as an impending public health threat. This bibliometric analysis elucidates the knowledge landscape of sarcopenia research by synthesizing growth trajectories, collaborative networks, and intellectual structures within the literature. Scientific publications spanning 1993–2023 were retrieved from the Web of Science and Scopus databases. VOSviewer, Biblioshiny, and ScientoPy software tools facilitated visualization and analysis of bibliometric trends. Results showed that after a seminal 2010 consensus definition paper, sarcopenia publications increased over 20-fold by 2021, following an initial gradual growth and then exponential expansion. China led in output volume; however, Western nations exhibited higher international collaboration. Prolific institutions clustered within Asia and Europe, although Australian and Canadian centers were also represented, reflecting expanding global networks. Core journals were dispersed across clinical medicine, gerontology, and nutrition. A co-occurrence network analysis of keywords delineated three predominant research domains: physical disability, muscle diagnostic metrics, and clinical prognostic outcomes. Keywords like “mobility” in the disability domain reflect sarcopenia's functional impacts. This novel perspective comprehensively maps sarcopenia's evolving knowledge landscape, despite limitations in incorporating citations and text mining. Practical contributions include identifying key areas for further research, including consolidating diagnostic methods through collaborative initiatives, exploring lifestyle interventions, and investigating sarcopenia across diverse specialties. By elucidating trends in growth, collaboration, and intellectual structure, this analysis offers data-driven perspectives to strategically combat this expanding public health challenge. The synthesis of publication trends provides both a novel scientometric perspective and practical insights to inform future sarcopenia research and guide public health policy.
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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.087 | 0.080 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.004 |
| 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".