Mapping research landscapes: a bibliometric and visual analysis of ketogenic diet interventions in liver health (2013–2024)
Bibliographic record
Abstract
Objective Bibliometric and visual analysis in the field of ketogenic diet (KD) on Liver Health from 2013 to 2024. Methods We retrieved the articles published between 2013 and 2024 from the Web of Science database and the Scopus database, and conducted the analysis using R software and VOSviewer software. Results The number of publications in this field shows an increasing trend year by year. The United States leads in the number of published articles, followed closely by China, Italy, Japan, and Canada. Notably, the United States has also excelled in international collaboration, with institutions like Sapienza University of Rome and the University of California, San Francisco, actively engaging with other global institutions. Nutrients has the highest publication frequency, while Cell Metabolism leads in citations. Key researchers such as Crawford PA and Watanabe M have emerged, with prominent keywords including obesity, Metabolic Associated Steatosis Liver Disease, NAFLD, beta-hydroxybutyrate, and low carbohydrate diet, indicating the central themes and trends in KD related Liver Health research. Conclusion KD, a novel dietary therapy designed to induce physiological ketosis, is anticipated to achieve significant advances in liver health. Global interest in this approach is increasing, underscoring its potential as an emerging therapeutic trend. This study offers a thorough analysis of the current research landscape and key hotspots related to the KD in liver health, providing valuable insights for future investigations.
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 | 0.016 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.227 | 0.215 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".