Research trends and hotspots in exercise interventions for liver cirrhosis: A bibliometric analysis via CiteSpace
Bibliographic record
Abstract
Cirrhosis is a chronic liver disease with severe consequences for a patient's health and survival. Exercise is an essential therapeutic strategy for both cirrhosis prevention and treatment. On the other hand, information regarding the present status of exercise-related research in cirrhosis is limited. Therefore, this study seeks to close the information gap in the scientific literature by using bibliometric techniques to analyze the trends, focal points, and cutting-edge research areas on exercise and cirrhosis. On September 22, 2023, research articles and reviews on exercise intervention for cirrhosis were obtained and downloaded from the Web of Science Core Collection (WoSCC). Subsequently, we employed CiteSpace (version 6.1.R6) to conduct bibliometric and knowledge graph analyses. 588 papers in 301 scholarly journals were written by 673 authors from 460 institutions spread over 63 countries and regions. The most productive nation among them is the United States. Not only is Zobair M. Younossi 1 of the most prolific writers, but he also receives the most co-citations. Most articles were published by the University of Michigan in the US, with the University of Alberta in Canada coming in second. Meanwhile, the WORLD JOURNAL OF GASTROENTEROLOGY has the most published articles, whereas HEPATOLOGY has the greatest number of co-citations. Apart from the theme words, the most frequently utilized keywords were "quality of life," "insulin resistance," and "mortality." Future research may concentrate on "obesity," "sarcopenia," and "Mediterranean diet," according to the analysis of keyword emergence. CiteSpace is used in this work to visually represent the topic of exercise intervention in cirrhosis, offering valuable information to researchers regarding the field's current status and possible future direction.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.203 | 0.257 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".