Bibliometric analysis of research trends in relationship between sarcopenia and surgery
Bibliographic record
Abstract
Background: The relationship between sarcopenia and surgery has attracted an increasing number of researchers in recent years. Our study aimed to identify the current research hotspot and status in this field by using bibliometric and visualization analysis. Methods: Publications about the relationship between sarcopenia and surgery that met the inclusion criteria were collected from the Science Citation Index Expanded. The bibliometric and visualized studies were performed using VOSviewer, and R. Results: A total of 2,261 documents on the relationship between sarcopenia and surgery were included in our study. These articles were written by 13,757 authors from 2,703 institutions in 70 countries and were published in 772 journals. The USA is the most productive and influential country in this field (524 publications and 15,220 citations). The Udice French Research Universities was the most productive institution in this field (57 publications), and the University of Alberta had the largest number of citations. Annuals of Surgical Oncology published the most studies in this field. Shen Xian was the most productive author in this field (number of publications = 19), and Baracos Vickie was the most influential author, whose studies in this field had been cited 2,209 times. The cluster analysis was performed and visualized, and the keywords were classified into 6 clusters: Cluster 1 (body composition and nutrition), Cluster 2 (sarcopenia), Cluster 3 (malnutrition and cachexia), Cluster 4 (cancer surgery), Cluster 5 (elderly and frailty), Cluster 6 (neuromuscular scoliosis). Conclusion: The relationship between sarcopenia and surgery was still a controversial and well-discussed topic in recent years. Our study showed that the study in this field mainly focused on sarcopenia, oncology surgery, orthopedics, and nutrition.
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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: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.493 | 0.496 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".