Extracellular vesicles as a new frontier of diagnostic biomarkers in osteosarcoma diseases: a bibliometric and visualized study
Bibliographic record
Abstract
The use of liquid biopsy in cancer research has grown exponentially, offering potential for early detection, treatment stratification, and monitoring residual disease and recurrence. Exosomes, released by cancer cells, contain tumor-derived materials and are stable in biofluids, making them valuable biomarkers for clinical evaluation. Bibliometric research on osteosarcoma (OS) and exosome-derived diagnostic biomarkers is scarce. Therefore, we aimed to conduct a bibliometric evaluation of studies on OS and exosome-derived biomarkers. Using the Web of Science Core Collection database, Microsoft Excel, the R "Bibliometrix" package, CiteSpace, and VOSviewer software, quantitative analyses of the country, author, annual publications, journals, institutions, and keywords of studies on exosome-derived biomarkers for OS from 1995 to 2023 were performed. High-quality records (average citation rate ≥ 10/year) were filtered. The corresponding authors were mainly from China, the USA, Australia, and Canada. The University of Kansas Medical Center, National Cancer Center, Japan, and University of Kansas were major institutions, with limited cooperation reported by the University of Kansas Medical Center. Keyword analysis revealed a shift from cancer progression to mesenchymal stem cells, exosome expression, biogenesis, and prognostic biomarkers. Qualitative analysis highlighted exosome cargo, including miRNAs, circRNAs, lncRNAs, and proteins, as potential diagnostic OS biomarkers. This research emphasizes the rapid enhancement of exosomes as a diagnostic frontier, offering guidance for the clinical application of exosome-based liquid biopsy in OS, contributing to the evolving landscape of cancer diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| 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; a candidate call from one teacher head, 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".