Bibliometric Analysis of the Top-Cited Publications and Research Trends for Stereotactic Body Radiotherapy
Bibliographic record
Abstract
OBJECTIVE: This study aims to analyze the 100 most cited papers and research trends on stereotactic body radiotherapy (SBRT). METHODS: We used Web of Science to identify the 100 most frequently cited papers on SBRT on September 29, 2021 and extracted the following data: publication year, source title, country/region, organization, total citations, and average number of citations per year. The research type and research domain were classified independently by the authors. Then we carried out a bibliometric analysis to determine the trends in research on SBRT. RESULTS: These 100 papers were cited a total of 26,540 times, and the median number of citations was 190 (range, 138-1688). "Stereotactic body radiation therapy for inoperable early stage lung cancer" by Timmerman et al. had the highest number of total citations (1688 times). International Journal of Radiation Oncology, Biology, Physics published the largest number of papers (37 papers), followed by Journal of Clinical Oncology (13 papers). The USA contributed the most papers (67 papers), followed by Canada (18 papers). Primary lung cancer (33 papers, 10,683 citations) and oligometastases (30 papers, 7,147 citations) were the most cited research areas. CONCLUSIONS: To the best of our knowledge, this is the first bibliometric analysis of the most frequently cited papers on SBRT. Our results provide insight into the historical development of SBRT and important advances in its application to cancer treatment. Early-stage non-small-cell lung cancer and oligometastases were the most cited research areas in the top 100 publications on SBRT, and SBRT combined with immunotherapy was a hot topic in the past few years. This study is helpful for researchers to identify the most influential papers and current research hotspots on SBRT.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.076 | 0.176 |
| 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, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".