Global trends and knowledge-relationship of symptom clusters in cancer research: a bibliometric analysis over the past 20 years
Bibliographic record
Abstract
Abstract Objective To use CiteSpace and VOSviewer to investigate the scientific production in the field of symptom clusters in cancer research. Methods The search was performed using the terms “symptom clusters,” “cancer,” and “oncology” on the Web of Science Core Collection database. The retrieval time was from 2001 to 2021, which covers the last 2 decades. Based on the production theory of scientific knowledge and the data mining of citations, data pertaining to the annual publications, journals, countries, organizations, authors, and keywords that produce symptom clusters in cancer research, as well as their cooperation (collaboration network), were extracted, and then both were clarified by the software tools VOSviewer (version 1.6.16) and CiteSpace (version 6.1.R2). Results A total of 1796 publications were retrieved between 2001 and 2021, and 473 relevant publications were included after screening. The results showed an increasing trend in published articles. The United States had the largest number of publications (261/473, 55.18%), followed by China and Canada. The University of California, San Francisco, was the most productive institution. Current research hotspots included the analysis of symptom clusters and symptom management in patients with breast cancer and lung cancer, as well as any advanced cancer and cancer cachexia; fatigue-related symptom clusters and depression-anxiety symptom cluster; and the impacts of symptom clusters on quality of life. The research frontiers included analysis between health-related quality of life and symptom clusters, data mining in symptom clusters, research on the mental health status of cancer patients, and study of the mechanism and biological pathways of symptom clusters. Conclusions The study provides insight into the global research perspective for the scientific progress on cancer symptom clusters, which suggests a growing scientific interest in this field, and more studies are warranted to guide symptom management.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.119 | 0.199 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".