Visual Analysis of Hot Topics and Trends in Nutrition for Decompensated Cirrhosis Between 1994 and 2024
Bibliographic record
Abstract
OBJECTIVE: An updated summary of the research profile of nutrition for the last 30 years for decompensated cirrhosis is lacking. This study aimed to explore the literature on nutrition for decompensated cirrhosis, draw a visual network map to investigate the research trends, and provide suggestions for future research. The Web of Science database retrieves the literature on nutrition for decompensated cirrhosis between 1994 and 2024. METHODS: We used the cooperative, co-occurrence, and co-citation networks in the CiteSpace knowledge graph analysis tool to explore and visualize the relevant countries, institutions, authors, co-cited journals, keywords, and co-cited references. RESULTS: We identified 741 articles on nutrition for decompensated cirrhosis. The number of publications and research interests has generally increased. The USA contributed the largest number of publications and had the highest centrality. The University of London ranked first in the number of articles issued, followed by the University of Alberta and Mayo Clinic. TANDON P, a "core strength" researcher, is a central hub in the collaborative network. Of the cited journals, HEPATOLOGY had the highest output (540, 15.3%). CONCLUSIONS: Over the past three decades, the focus of research on nutrition in decompensated cirrhosis has shifted from "hepatic encephalopathy, intestinal failure, metabolic syndrome, and alcoholic hepatitis" to "sarcopenia and nutritional assessment." In the future, nutritional interventions for sarcopenia should be based on a multimodal approach to address various causative factors. Its targeted treatment is an emerging area that warrants further in-depth research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.048 | 0.033 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".