Stereotactic Body Radiotherapy for Prostate Cancer
Bibliographic record
Abstract
OBJECTIVES: To provide an overview of the achievements and future research with stereotactic body radiotherapy (SBRT) in prostate cancer. METHODS: SBRT publications for prostate cancer were retrieved from the Web of Science and Dimension database. Bibliometric analyses were performed using VOSviewer and Prism graph. Analysis of variance test was used to compare the publication, citation, and the mean citation between specialty journals. Network maps were produced to identify authors' and countries' collaboration clusters. RESULTS: Between 2006 and 2020, 574 publications fulfilling the inclusion criteria were identified, and a significant growth trend in publication (P<0.0001) and citation (P=0.001) number was recognized over the period. The United States was the most productive country with 253 (44.2%) articles. The RED Journal had the highest number of publications (14%) and citations (19%). Urology journals published (P=0.01) and cited significantly less than radiation oncology journals (P=0.01). All open access and non-open access number of publications increased over time, with a significant difference between non-open access and open access journals (P<0.0001). Two author clusters were identified, in the United States with the collaboration of Canadian and British authors, and in Italy with the participation of European authors. CONCLUSION: The number of publications and citations on SBRT for prostate cancer has grown linearly in the last decades. The United States is the leading country in this research field, and the use of SBRT in oligometastatic disease, reirradiation, and salvage seems to be hot topics in this research field.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".