Bibliometric and Visual Analysis of Palliative Nutrition Research Based on Web of Science
Bibliographic record
Abstract
Objective: Nutritional therapy has been shown to reduce the mortality rates of critically ill individuals. In recent years, there has been a significant increase in scholarly curiosity about the growing use of palliative nutrition. In order to determine the global research output on palliative nutrition, this bibliometric analysis was conducted to assess the current status of research trends and research directions. Methods: The bibliometric data of the study was obtained from the online database Web of Science and analyzed and visualized with Excel, the Bibliometrix R package (version 4.1.2), and the bibliometric online application (https://bibliometric.com/app) tools. Results: A total of 1067 publications were included in this study. The majority of publications (398,37.30%) and citations (n: 9252) in this discipline have come from the United States. The most frequent publication type detected was article (n: 794). Publications published in 398 different sources (journals/books etc.). The international co-authorship rate was 11.62%. In the last 20 years, the annual number of publications has drastically expanded. The highest number of publications was published in 2020 and 2021 (n: 67, and n: 64 respectively). Australia, France, Canada, Japan, and China stand out as the countries with the highest number of publications in recent years. The terms 'end, care, hydration, nutrition, life, decision-making, artificial nutrition, and palliative care' were the most preferred keywords. Conclusion: Finally, given the number of palliative care patients globally is expected to rise, it is critical to do ongoing research on appropriate nutritional therapy for these patients. As our study shows study gaps and study trends, it can provide insight for future work in this field.
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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 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.234 | 0.229 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".