Research progress and frontier trends in liver cancer immunotherapy in the post-COVID-19 era (2020–2024): a visualization analysis based on bibliometric methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.038 | 0.092 |
| 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) | 0.001 | 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 it