Global research trends in palliative care for breast cancer from 2012 to 2022: A scientometric analysis
Bibliographic record
Abstract
Purpose We used bibliometric methods to evaluate the global scientific output of palliative care breast cancer research and to explore the current status and further research directions in the field over the past decade. Methods All relevant publications from the year 2012 to 2022 were retrieved from Web of Science. We applied VOSviewer and Bibliometrix R v4.2.1 to obtain information on subject domains, annual publication output and citations, countries and authors with the highest productivity, influential journals and articles, and popular keywords. Results In total, 1529 publications were included in the final analysis. Health services and supportive care, pain and symptom management were the focus of the research in the field. From the year 2017 to 2021, the annual publication output was abundant and peaked in 2018. The systematic review by Fitzmaurice et al. in 2017 was the most-cited publication. The United States was the leading country with the maximum number of publications, citations, and link strengths with other countries. The most contributing institution was University of Toronto. E. Bruera was the most prolific author, ranking first among the authors in both the H and M index. The journal with the most publications was Palliative & Supportive Care. Keywords analysis indicated that exploring the significant degree of palliative care to reduce anxiety and depression in breast cancer patients may be a good research direction. In addition, how to assess the optimal timing of palliative care interventions and further exploring the specific contradiction between insufficient medical resources and palliative care are also possible research directions. Conclusion Palliative care plays an important role in the treatment of breast cancer. With the help of a scientometric analysis in this field, researchers can clarify the current research status and hotspots worth fully exploring.
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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 | 0.021 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.217 | 0.293 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| 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".