Advances in Breast Cancer Management and Extracellular Vesicle Research, a Bibliometric Analysis
Bibliographic record
Abstract
Extracellular vesicles transport variable content and have crucial functions in cell-cell communication. The role of extracellular vesicles in cancer is a current hot topic, and no bibliometric study has ever analyzed research production regarding their role in breast cancer and indicated the trends in the field. In this way, we aimed to investigate the trends in breast cancer management involved with extracellular vesicle research. Articles were retrieved from Scopus, including all the documents published concerning breast cancer and extracellular vesicles. We analyzed authors, journals, citations, affiliations, and keywords, besides other bibliometric analyses, using R Studio version 3.6.2. and VOSviewer version 1.6.0. A total of 1151 articles were retrieved, and as the main result, our analysis revealed trending topics on biomarkers of liquid biopsy, drug delivery, chemotherapy, autophagy, and microRNA. Additionally, research related to extracellular vesicles in breast cancer has been focused on diagnosis, treatment, and mechanisms of action of breast tumor-derived vesicles. Future studies are expected to explore the role of extracellular vesicles on autophagy and microRNA, besides investigating the application of extracellular vesicles from liquid biopsies for biomarkers and drug delivery, enabling the development and validation of therapeutic strategies for specific cancers.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.100 | 0.127 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".