Bibliometric Analysis of Research on Exercise Intervention for Cancer-Related Cognitive Impairments
Bibliographic record
Abstract
Introduction: Cancer treatments frequently lead to cognitive impairments, affecting a substantial global population. Among various approaches, exercise has emerged as a promising strategy for rehabilitation. However, a comprehensive bibliometric analysis of research in this field is lacking. Methods: We conducted a bibliometric analysis of 10,345 articles sourced from the Web of Science database using the R package “bibliometrix”. Our analysis examined publication trends, leading countries, journals, authors, institutions, keywords, and prevalent themes. Results: Over the past two decades, research on exercise interventions for cancer-related cognitive impairments (CRCI) has advanced significantly. Nonetheless, challenges persist in elucidating underlying mechanisms, developing innovative strategies, and creating effective tools. Conclusions: The number of publications notably increased from 1998 to 2023, although there has been a recent decline in citations. The United States (US) leads in both publications and citations, while China is showing increasing influence. Using Lotka’s Law in our bibliometric analysis, we identified 58 key authors in the field of exercise interventions for CRCI. Leading institutions such as the University of Toronto and Duke University are at the forefront of this research. Although the Journal of Clinical Oncology has fewer publications, it remains influential. Current research focuses on exercise interventions to enhance the quality of life for cancer patients, with particular emphasis on cognitive rehabilitation in breast cancer and the challenges faced by survivors. Future research should delve deeper into intervention mechanisms, behavioral strategies, telemedicine, and precise cognitive assessment tools.
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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 | 0.034 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.251 | 0.325 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| 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, 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".