Trends in Diet and Cancer Research: A Bibliometric and Visualization Analysis
Bibliographic record
Abstract
Diet plays a critical role for patients across the cancer continuum. The World Cancer Research Fund International and the American Cancer Society have published evidence supporting the role of nutrition in cancer prevention. We conducted an analysis of the literature on dietary nutrients and cancer to uncover opportunities for future research. The objective of the bibliometric analysis was to describe trends in peer-reviewed publications on dietary components and cancer and to highlight research gaps. PubMed was queried for manuscripts with diet- and cancer-related keywords and Medical Subject Headings (MeSH) terms. Metadata covering 99,784 publications from 6469 journals were analyzed to identify trends since 1970 on diet topics across 19 tumor types. Publications focused largely on breast, colorectal, and liver cancer, with fewer papers linking diet with other cancers such as brain, gallbladder, or ovarian. With respect to "unhealthy" diets, many publications focused on high-fat diets and alcohol consumption. The largest numbers of publications related to "healthy" diets examined the Mediterranean diet and the consumption of fruits and vegetables. These findings highlight the need for additional research focused on under-investigated cancers and dietary components, as well as dietary studies during cancer therapy and post-therapy, which may help to prolong survivorship.
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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 | 0.024 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.176 | 0.252 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".