Global trends of research on depression in breast cancer: A bibliometric study based on VOSviewer
Bibliographic record
Abstract
Background: Depression is common psychiatric morbidity in breast cancer survivors, seriously affecting patients' quality of life and mental health. A growing body of research has investigated depression in breast cancer. However, no visual bibliometric analysis was conducted in this field. This study aimed to visualize the literature to identify hotspots and frontiers in research on breast cancer and depression. Methods: The publications related to depression in breast cancer were retrieved in the Web of Science Core Collection between 1 January 2002 and 17 March 2022. VOSviewer was used to identify co-occurrences and collaborations among countries, institutions, and keywords. CiteSpace was used to detect keyword bursts. Results: A total of 7,350 articles and reviews related to depression in breast cancer were identified. From 2002 to 2022, the United States and the People's Republic of China were the most productive countries in this field. The University of California, Los Angeles, and the University of Toronto were the most productive institutions in this field. The Journal of Psycho-oncology, followed by Supportive Care in Cancer and the Journal of Clinical Oncology, had the most publications on depression in breast cancer. Of the top 10 journals, seven were from the United States, two were from England, and one was from Germany. Five research hotspots of depression in breast cancer were identified by co-word analysis. Research on post-traumatic growth, spiritual interventions, cognitive-behavioral therapy, physical activity, and symptom cluster management of depression in breast cancer was relatively mature in the core hotspots. Burst detection of keywords on depression in breast cancer showed the latest hotspots, such as mental health, cancer survivor mortality, and activity. Conclusion: The research on depression in breast cancer is growing. Attention should be paid to the latest hotspots, such as mental health, cancer survivor, mortality, exercise, and physical activity.
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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 | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.012 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.184 | 0.264 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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.
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".