Knowledge domain and emerging trends in brachytherapy: A scientometric analysis
Bibliographic record
Abstract
Abstract Objective Assessing the current scientific situation helps to recognize the gaps and strengths of brachytherapy research projects. This research project was conducted to assess the knowledge domain and emerging trends in brachytherapy through a scientometric perspective. Methods For the present research, the Web of Science database was considered as the data source. Integrated data was transferred to Bibliometrix R Package V3.1. In this study, the scientometric approach was performed by CiteSpace 5.8.R3 to draw the trends and signify issues in the research area. Eventually, scientometric indicators were evaluated at the level of authors, documents, journals, organizations, and countries. Results A total of 31,362 documents from 64,740 Independent researchers were retrieved. The United States, Germany, and Canada were the most active countries in brachytherapy‐related research projects. In the present study, Luc Beaulieu, Christine Kirisits, and Ronald Nath were identified as the most influential authors. Eventually, keywords clusters were constructed by using the method of co‐citation analysis. In this case, the main clusters were cervical cancer and prostate cancer. Conclusion Assessing the scientific trends in brachytherapy indicated that new insights have been gained into this cancer treatment technique. In this case, development of computer applications and artificial intelligence alongside deep learning utilization provides new horizons for oncology and radiotherapy researchers.
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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 | 0.016 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.131 | 0.127 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".