Global research trends in radiotherapy for bone metastases: a systematic bibliometric analysis
Bibliographic record
Abstract
Background: With the development of various advanced radiotherapy techniques, research related to radiotherapy for bone metastases has made great progress, and scholars have published a large number of publications. In this study, we summarized the knowledge structure of radiotherapy for bone metastases and outlined the research hotspots through bibliometric analysis. Methods: Publications on radiotherapy for bone metastases from 1992 to 2024 were searched in the Web of Science Core Collection (WoSCC) database. Countries, institutions, authors, references, and keywords in the field were visualized using VOSviewer version 1.6.19 and CiteSpace version 6.3.R1. Results: 1303 publications from 71 countries were included in this study. The number of research publications on radiotherapy for bone metastases has been increasing year by year. The United States of America (USA) ranking first in terms of publication count and co-citation frequency. The most prolific institutions and authors were the University of Toronto and Sahgal A, while Chow E was the most co-cited author. The most co-cited paper was published by Lutz S et al. in 2011 in Internation Journal Of Radiation Oncology Physics. "stereotactic body radiotherapy", "spine metastases", "spinal cord compression", " immunotherapy" and "oligometastases" are the main keywords of the current research topics. Conclusions: The application of stereotactic body radiotherapy (SBRT) in the treatment of patients with bone metastases, especially oligometastases, has attracted extensive attention from researchers. How to choose reasonable radiotherapy for patients with complicated bone metastases has now become a research hotspot. Radiotherapy combined with immunotherapy may be the future development trend.
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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 | 0.019 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.171 | 0.212 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".