Bibliometric and visualization analysis of radiation brain injury from 2003 to 2023
Bibliographic record
Abstract
Background: Over the past two decades, the field of radiation brain injury has attracted the attention of an increasing number of brain scientists, particularly in the areas of molecular pathology and therapeutic approaches. Characterizing global collaboration networks and mapping development trends over the past 20 years is essential. Objective: The aim of this paper is to examine significant issues and future directions while shedding light on collaboration and research status in the field of radiation brain injury. Methods: Bibliometric studies were performed using CiteSpaceR-bibliometrix and VOSviewer software on papers regarding radiation brain injury that were published before November 2023 in the Web of Science Core Collection. Results: In the final analysis, we found 4,913 records written in 1,219 publications by 21,529 authors from 5,007 institutions in 75 countries. There was a noticeable increase in publications in 2014 and 2021. The majority of records listed were produced by China, the United States, and other high-income countries. The largest nodes in each cluster of the collaboration network were Sun Yat-sen University, University of California-San Francisco, and the University of Toronto. Galldiks N, Barnett GH, Langen KJ and Kim JH are known to be core authors in the field. The top 3 keywords in that time frame are radiation, radiation necrosis, and radiation-therapy. Conclusions: The objective and thorough bibliometric analysis also identifies current research hotspots and potential future paths, providing a retrospective perspective on RBI and offering useful advice to researchers choosing research topics. Future development directions include the integration of multi-omics methodologies and novel imaging techniques to improve RBI's diagnostic effectiveness and the search for new therapeutic targets.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.118 | 0.155 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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; both teacher heads agree on what is shown here.
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".