Bibliometric Analysis of the Relationship Between Breast Cancer and Exercise Based on Citespace
Bibliographic record
Abstract
This study aims to conduct a bibliometric analysis of the literature on the relationship between breast cancer and exercise using Citespace software, in order to explore the research hotspots, trends, and deficiencies in this field. Relevant literature including keywords such as “breast cancer” and “exercise” was retrieved from the Web of Science database and analyzed using Citespace software for data processing and visualization. The main conclusions of this study are as follows: In co-occurrence analysis of keywords, researchers mainly focus on the prevention, rehabilitation, and treatment of breast cancer, as well as the effects of different types of exercise on breast cancer. Meanwhile, the cooccurrence patterns of keywords in studies related to exercise and breast cancer indicate an increasing attention to the relationship between breast cancer and exercise, making it gradually one of the hotspots in breast cancer research. The timeline of keywords and burst detection analysis show that researchers have increasingly focused on the relationship between breast cancer and exercise in recent years, with research intensity showing an increasing trend year by year. Furthermore, in recent years, researchers have begun to emphasize the role of exercise in breast cancer rehabilitation, rather than just prevention and treatment. Discipline analysis indicates that research in this field involves multiple disciplinary areas, including medicine, oncology, sports science, nutrition, etc. Additionally, an analysis of papers published by country reveals that the United States leads in research on the relationship between breast cancer and exercise, followed closely by the United Kingdom, China, Canada, Australia, and other countries. Conclusion: Literature on the relationship between breast cancer and exercise shows a trend of increasing quantity year by year. However, there are still some deficiencies in this field, such as lack of uniform research methods, overly limited research samples, and insufficient rigor in research results. Therefore, further strengthening research on the relationship between breast cancer and exercise is highly necessary.
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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 | 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 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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.239 | 0.229 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".