Research progress of breast cancer surgery during 2010–2024: a bibliometric analysis
Bibliographic record
Abstract
Purpose: This study seeks to systematically analyze the research literature pertaining to breast cancer surgery from 2010 to 2024, as indexed in the PubMed database, employing bibliometric methodologies. Methods: Employing the "bibliometrix" package in the R programming language, alongside VOSviewer and CiteSpace software, this research conducted a comprehensive visual analysis of 1,195 publications. The analysis encompassed publication trends, collaborative networks, journal evaluation, author and institutional assessments, country-specific analyses, keyword exploration, and the identification of research hotspots. Results: The study observed a rising trend in the number of publications related to breast cancer surgery. However, there was a concomitant decline in citation rates, potentially indicating either a saturation of the research field or a diminution in research quality. The United States, China, and Japan are the leading contributors to research output, with the United States showing the most extensive international collaboration. The University of California, University of Toronto, and University of Texas MD Anderson Cancer Center were the top institutions for the number of published papers. Through a comprehensive analysis of keywords, we have identified "breast cancer" "pain" "anxiety" "lymphedema" "mastectomy" and "surgery" as central research themes within this domain, the corresponding clusters were subjected to analysis. Conclusion: This study provides a comprehensive review of breast cancer surgery research, emphasizing major research areas and proposing future research directions. This study provides a significant resource for researchers and clinicians in the 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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.125 | 0.147 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".