Global Status and Trends of Tumor Hypoxia: A Scientometric Analysis
Bibliographic record
Abstract
Objective This study aimed to explore the status quo, hot topics, and future prospects in the field of tumor hypoxia by scientometric analysis. Methods The literatures about tumor hypoxia were downloaded from the Web of Science Core Collection from inceptions to Dec.31st. 2018. We used CiteSpace 5.4.R1, VOSviewer 1.6.10 and Excel 2016 to analyze literature information, such as country, institution, author, keywords and references. Results A total of 4399 papers about tumor hypoxia were identified, involving 20157 authors from 3604 institutions in 84 countries. Vaupel P, Harris AL and Dewhirst MW published the most literatures. United States, China and United Kingdom contributed the most publications. The three most contributed institutions are the University of Toronto, Stanford University and Oxford University. International Journal of Radiation Oncology Biology Physics (n=175, IF2017=5.554), Cancer Research (n=144, IF2017=9.13) and Radiotherapy Oncology (n=114, IF2017=4.942) are the most productive journals. The main hot topics in tumor hypoxia field are tumor hypoxic cells and cytokines, tumor hypoxia therapy, tumor hypoxia diagnosis, tumor hypoxia prognosis. Conclusion Developed countries in Europe and America are dominant in the field of tumor hypoxia research. The diagnosis and treatment of tumor hypoxic is still a difficult problem, especially how to overcome the drug resistance caused by tumor hypoxic.
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How this classification was reachedexpand
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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.089 | 0.118 |
| 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) | 0.005 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".