A bibliometric analysis of liver cancer bone metastases: advances in mechanisms of occurrence and treatment options
Bibliographic record
Abstract
Background: Liver cancer ranks as the sixth most frequently diagnosed cancer and is the third leading cause of cancer-related deaths worldwide. Its poor prognosis is mainly due to tumor metastasis. Bone metastasis commonly occurs in the advanced stages of liver cancer and can significantly impact patients' quality of life and prognosis. However, current treatment methods for bone metastasis resulting from liver cancer have significant limitations. This article aims to review the latest research and trends in global studies on bone metastasis associated with liver cancer. Methods: This study used the Web of Science Core Collection to gather 3,347 articles on liver cancer with bone metastasis published from 2004 to 2024.The study applied bibliometric analysis methods, including CiteSpace, VOSviewer, and other tools, to examine countries, institutions, journal authors, keywords, and references. Results: From 2004 to 2024, we collected 3,845 records, including 3,347 articles and 498 reviews from 92 countries. Publications had shown exponential growth, peaking in 2021 with an annual growth rate of 8.3%. The research involved 13,188 organizations. China had the most publications (n=970, 29%), followed by the United States (n=648, 19.4%), Japan (n=317, 9.5%), and Germany (n=228, 6.8%); Canada had a notably higher rate of international collaboration at 44.8%. In total, 830 journals contributed to this research. The most cited journal was "J Clinical Oncol" with 4,275 citations, while "New Engl J Med" had the highest impact factor at 158.5. The research involved 23,710 authors, with Wang, Xin and Zhang, Chao being the authors with the most publications. Wang Z published the first study on this topic in 2007. In 2008, Zhang Y, Wang Y, and Zhang J also began researching this area and have continued to be actively involved since then. The study referenced a total of 79,935 sources, with the paper by Sung H et al., published in CA Cancer J Clin in 2021, achieving the highest citation rate of 42.46. Conclusion: This study systematically summarizes research findings on liver cancer with bone metastasis from 2004 to 2024, It also identifies and predicts global research hotspots and trends. In the future, the molecular mechanisms of liver cancer with bone metastasis will be a major research topic. The expansion of surgical treatment options and the advancement of innovative therapies will benefit more patients.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.096 | 0.087 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".