Innovation Status of Gene Therapy for Breast Cancer
Bibliographic record
Abstract
BACKGROUND: To analyze multi-source data including publications and patents, and try to draw the whole landscape of the research and development community in the field of gene therapy for breast cancer. MATERIALS AND METHODS: Publications and patents were collected from the Web of science and databases of the five major patent offices of the world, respectively. Bibliometric methodologies and technology are used to investigate publications/patents, their contents and relationships. RESULTS: A total of 2,043 items published and 947 patents from 1994 to 2013 including "gene therapy for breast cancer" were retrieved. The top five countries in global publication share were USA, China, Germany, Japan and England. On the other hand, USA, Australia, England, South Korea and Japan were the main producers of patents. The universities and enterprises of USA had the highest amount of publication and patents. Adenovirus- and retrovirus-based gene therapies and small interfering RNA (siRNA) interference therapies were the main topics both in publications and patents. CONCLUSIONS: The above results show that global research in the field of gene therapy for breast cancer is increasing and the main participants in this field are USA and Canada in North America, China, Japan and South Korea in Asia, and England, Germany, and Italy in Europe. Also, this article demonstrates the usefulness of bibliometrics to address key evaluation questions and define future areas of research.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.039 | 0.061 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".