Data_Sheet_3_Mapping the evolution and impact of ketogenic diet research on diabetes management: a comprehensive bibliometric analysis from 2005 to 2024.CSV
Bibliographic record
Abstract
Objective The ketogenic diet (KD) has been explored for diabetes management; however, a quantitative synthesis of its specific effects on diabetes has not yet been conducted. This study aims to examine the current status and research hotspots of KD in diabetes management from 2005 to 2024, providing a reference for future research. Methods We retrieved articles published between 2005 and 2024 from the Web of Science database and analyzed them using R software, VOSviewer, and CiteSpace. Results This study includes 432 relevant publications. From 2005 to 2024, the volume of literature in this field has shown a steady upward trend, with a notable increase from 2017 to 2021, and a slight decline observed from 2021 to 2023. The United States is the leading country in terms of the number of publications, followed by China, Australia, and Canada. The United States not only leads in publication volume but also maintains a broader international collaboration network. Nutrients and the American Journal of Clinical Nutrition are the most frequently published and cited journals. Current research hotspots primarily focus on the impact of KD on blood glucose control, insulin resistance, and lipid metabolism in diabetic patients. Mechanistic studies on KD in diabetes management concentrate on aspects such as the “regulation of genes by β-hydroxybutyrate,” “anti-inflammatory effects,” and “oxidative stress.” The role of the gut microbiome is also emerging as an important research area. Currently, exploring the application of KD in managing different age groups and types of diabetes has become a significant research trend. Conclusion As an emerging dietary intervention, KD is gradually attracting widespread attention from researchers around the world and is expected to become a major research focus in the future for diabetes management and control. This paper provides a systematic review and analysis of the current research status and hotspots of KD in diabetes management, offering important references and insights for future research in related fields.
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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: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.210 | 0.336 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.118 | 0.082 |
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".