Research progress and frontier trends in liver cancer immunotherapy in the post-COVID-19 era (2020–2024): a visualization analysis based on bibliometric methods
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has transformed liver cancer research, making immunotherapy a key breakthrough for advanced cases. This study uses bibliometrics to reveal research hotspots and paradigm shifts of this field from 2020 to 2024. METHODS: The data were retrieved from the Web of Science Core Collection (2020 to 2024). Data quality was ensured through two rounds of independent data cleaning, achieving a Kappa coefficient of 0.89. A comprehensive bibliometric analysis was conducted on the literature related to liver cancer immunotherapy, utilizing tools such as Biblioshiny, VOSviewer, Scimago Graphica, CiteSpace, and Microsoft Office Excel (2022 version). RESULTS: From 2020 to 2024, China and the US led global liver cancer immunotherapy research, forming a collaboration network with Canada. Sun Yat-sen University is a key hub with over 600 publications and an annual growth rate of 18.5%, closely collaborating with Huazhong University of Science and Technology and Zhejiang University (correlation index 0.92). The journal impact is dominated by Frontiers in Immunology and Frontiers in Oncology, which form a dual-core citation network, with the top 1% journals contributing 35.7% of publications. Frontiers in Immunology has the highest h-index (35) and fastest growth rate. The co-citation network includes three major clusters: immune mechanisms (led by Nature), clinical trials (Clinical Cancer Research), and liver cancer pathology (Hepatology). Research themes evolve in four directions: prognostic models, tumor microenvironment, hepatocellular carcinoma mechanisms, and cancer immunotherapy. Hotspots include "cancer", "immunotherapy", and "hepatocellular carcinoma", with rising trends in "tumor microenvironment" and "combination therapy" after 2021. CONCLUSION: The COVID-19 pandemic has spurred multidisciplinary integration and the use of real-world evidence in research. Emerging themes like Sino-American collaboration, knowledge diffusion in core journals, and tumor microenvironment are shaping liver cancer immunotherapy research in the post-pandemic era. This study offers strategic insights for optimizing resources and advancing clinical-basic translational research.
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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 | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.100 | 0.111 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".