Global trends in BRCA-related breast cancer research from 2013 to 2022: A scientometric analysis
Bibliographic record
Abstract
Introduction Since the mid-2000s, breast cancer incidence among women has slowly increased at about 0.5% per year. In the last three decades, Breast Cancer Susceptibility Gene ( BRCA ) has been proven to be the crucial gene in encouraging the incidence and development of breast cancer. However, scientometric analysis on BRCA -related breast cancer is in shortage. Thus, to have a clear understanding of the current status and catch up with the hotspots, a scientometric analysis was conducted on specific academic publications collected from the Web of Science (WoS). Methods We searched the Web of Science Core Collection (WoSCC) to procure associated articles as our dataset. Bibliometric, CiteSpace, VOSviewer, and HistCite software were then applied to conduct visual analyses of countries, institutions, journals, authors, landmark articles, and keywords in this research field. Results A total of 7,266 articles and 1,310 review articles published between 2013 to 2022 were retrieved eventually. The annual output steadily rose year by year and peaked in 2021. The USA led the way in the number of published works, total citations, and collaboration. Breast Cancer Research and Treatment was the most favoured journal in this research field. Narod SA from the University of Toronto produced the most publications. At last, the most prominent keywords were “breast cancer” (n=1,778), “women” (n=1,369), “ brca1 ” (n=1,276), “ovarian cancer” (n=1,259), “risk” (n=1,181), and “mutations” (n=929), which exposed the hotspots within the BRCA domain of breast cancer study. Conclusion The tendency in the BRCA research field over the past decade was presented by the scientometric analysis. The current research focus is the clinical trials of poly-adenosine diphosphate ribose polymerase inhibitors (PARPi) drugs and their resistance mechanisms.
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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 | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.016 | 0.090 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".