Bibliographic record
Abstract
There is no single pharmacologic therapy that has been approved to treat nonalcoholic fatty liver disease in the general population. The backbone of therapy currently includes intensive lifestyle modification with established targets for diet and weight loss. The use of unsweetened, unfiltered coffee along with limiting high fructose corn syrup have emerged as beneficial dietary recommendations. The use of empiric oral hypoglycemic agents and vitamin E, however, has not been widely accepted. Developing bariatric surgical techniques are promising, but additional studies with long-term follow up are needed before it can be widely recommended. Finally, liver transplantation is an increasingly frequent consideration once complications of end-stage disease have developed. The future treatment of those with nonalcoholic fatty liver disease will likely involve a personalized approach. The importance of the gut microbiome in mediating hepatocyte inflammation and intestinal permeability is emerging and may offer avenues for novel treatment. The study of anti-fibrotic agents such as pentoxifylline and FXR agonists hold promise and new pathways, such as hepatocyte cannabinoid receptor antagonists are being studied. With the incidence of obesity and the metabolic syndrome increasing throughout the developed world, the future will continue to focus on finding novel agents and new applications of existing therapies to help prevent and to mediate the progression of nonalcoholic fatty liver disease.
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 | 0.000 | 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 | 0.000 |
| 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 | 0.004 |
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".