Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) ranges from simple steatosis to nonalcoholic steatohepatitis (NASH), leading to fibrosis and potentially cirrhosis, and it is one of the most common causes of liver disease worldwide. NAFLD is associated with other medical conditions such as metabolic syndrome, obesity, cardiovascular disease and diabetes. NASH can only be diagnosed through liver biopsy, but noninvasive techniques have been developed to identify patients who are most likely to have NASH or fibrosis, reducing the need for liver biopsy and risk to patients. Disease progression varies between individuals and is linked to a number of risk factors. Mechanisms involved in the pathogenesis are associated with diet and lifestyle, influx of free fatty acids to the liver from adipose tissue due to insulin resistance, hepatic oxidative stress, cytokines production, reduced very low-density lipoprotein secretion and intestinal microbiome. Weight loss through improved diet and increased physical activity has been the cornerstone therapy of NAFLD. Recent therapies such as pioglitazone and vitamin E have been shown to be beneficial. Omega 3 polyunsaturated fatty acids and statins may offer additional benefits. Bariatric surgery should be considered in morbidly obese patients. More research is needed to assess the impact of these treatments on a long-term basis. The objective of this article is to briefly review the diagnosis, management and treatment of this disease in order to aid clinicians in managing these patients.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".