The development of cancer nutrition research from 2013 to 2022: a bibliometric and visualized analysis study
Bibliographic record
Abstract
Background: The use of nutrition in cancer treatment has become increasingly widespread in recent decades, and the current stage of nutritional support and assessment has had a positive effect on reducing the side effects of cancer treatment. Based on the analysis of international literature on “tumor nutrition,” we identified the current status of research, research hotspots, and frontiers and provided a theoretical basis and reference for the development of related research in China. This study aimed to conduct a bibliometric analysis of the global literature published from 2013 to 2022 to assess the current research directions. Methods: The Web of Science core collection was searched from 2013 to 2022. The VOSviewer 1.6.19 and CiteSpace 6.2.2 were adopted to conduct the analysis. Results: Following the inclusion and exclusion criteria, a total of 28,245 documents were collected. The number of articles issued annually was fluctuatingly increasing. These articles were written by 124,412 authors from 20,162 affiliations in 166 countries or regions and were published in 3110 journals. The leading authors were Susan M. Gapstur, Heiner Boeing, and Hanping Shi. All publications were taken from 166 countries/regions and 20,162 organizations. The most productive countries were the United States and China. The most active institutions were the Harvard Medical School and University of Alberta. A total of 3110 journals contributed to this field, and the leading journals were Nutrients and Clinical Nutrition. The important author keywords occurred most frequently were cancer, nutrition, risk, survival, mortality, and sarcopenia. Conclusions: This study provided the dynamics and progress of nutrition research field. The studies about nutrition are booming. The current growth trend predicts that the global field of oncology nutrition will still increase. In addition to the traditional research on tumor nutrition, the development of interdisciplinary research should be promoted. Every country should strengthen international cooperation to enhance the influence of research results and solve many common scientific problems in the field of tumor nutrition research in multiple dimensions.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.133 | 0.212 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".