Dietary Fiber and Cancer Management: A Twenty‐Five‐Year Bibliometric Review of Research Trends and Directions
Bibliographic record
Abstract
Background: The global burden of cancer necessitates innovative approaches to its management and treatment. Traditional treatments like radiotherapy, immunotherapy, surgery, and chemotherapy, carry significant side effects that impact patient quality of life. Dietary fiber has attracted research interest as a potential mitigator of cancer progression and a supportive agent in cancer treatment. This bibliometric study analyzes trends in research connecting dietary fiber, cancer therapy, and gut health from April 1999 to May 2024. Methods and Results: Web of Science, PubMed, and Scopus databases were used to retrieve peer‐reviewed publications from April 1999 to May 2024 on dietary fiber and cancer management. The study identifies a rising scholarly interest in dietary fiber’s role in cancer management, focusing significantly on the interactions between dietary fiber and gut microbiota. These interactions are particularly noted for their influence on inflammation and cancer metastasis. The study highlights evolving research themes, the importance of specific fiber types in cancer progression, and highlights persistent foundational themes like glycosylation. Emerging areas include personalized nutrition and innovative therapeutic approaches. The geographical and institutional contributions, mainly from the United States and China, underline the significance of collaborative and interdisciplinary efforts in advancing research. Conclusion: This analysis emphasizes integrating dietary strategies in comprehensive cancer care and aims to address research gaps to develop more effective and patient‐centered cancer therapy and prevention strategies.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.068 | 0.114 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".