Dietary factors in nonalcoholic fatty liver disease: impacts on human and animal health - a review
Bibliographic record
Abstract
Abstract Non-alcoholic fatty liver disease (NAFLD) is defined as a clinical syndrome characterized by excessive fat accumulation in liver, predominantly influenced by dietary choices. This study provides an extensive quantitative literature analysis on dietary influences on NAFLD. Bibliometric data were collected through the search string TOPIC = (“NAFLD*” OR “nonalcoholic fatty liver*” OR “non-alcoholic fatty liver*”) AND TOPIC = (“diet*” OR “nutrition*” OR “food*” OR “feed*”), which yielded 12,445 publications indexed within the Web of Science Core Collection. Utilizing VOSviewer software, term maps were generated to visually illustrate recurring phrases alongside citation data. The literature, which has seen exponential growth since the 2010s, predominantly consists of original articles, with a ratio of 4.7:1 compared to reviews. Notably, the significant contributors to this field were China and the United States. The majority of publications were found journals specialized in Gastroenterology & Hepatology, Nutrition & Dietetics, Biochemistry & Molecular Biology, Endocrinology & Metabolism, and Pharmacology & Pharmacy. Key dietary compounds/compounds classes such as resveratrol, polyphenols, curcumin, berberine, quercetin, flavonoids, omega-3 fatty acids, docosahexaenoic acid (DHA), genistein, and palmitic acid were frequently mentioned and cited. Many of them were demonstrated to have some potential benefits on NAFLD, both in human and animal studies.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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, 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".