Current and emerging therapies for alcohol-associated hepatitis
Bibliographic record
Abstract
Alcohol-related liver disease (ALD) encompasses a spectrum of diseases caused by excessive alcohol consumption. ALD includes hepatic steatosis, steatohepatitis, variable degrees of fibrosis, cirrhosis, and alcohol-associated hepatitis (AH), the latter being the most severe acute form of the disease. Severe AH is associated with high mortality (reaching up to 30%-50%) at 90 days. The cornerstone of ALD, and particularly AH, treatment continues to be abstinence, accompanied by support measures such as nutritional supplementation and management of alcohol withdrawal syndrome (AWS). In severe AH with model for end-stage liver disease (MELD) score ≥21, corticosteroids can be used, especially MELD score between 25 and 39, where the highest benefit is achieved. Other key aspects of treatment include the early identification of infections and their associated management and the proper identification of potential candidates for liver transplantation. The development of new therapies based on the pathophysiology and mechanisms of liver injury are underway. This includes the modulation and management of the innate immune response, gut dysbiosis, bacterial translocation, and bacteria-derived products from the intestine. These hold promise for the future of AH treatment.
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 | 0.001 | 0.001 |
| 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) | 0.007 | 0.002 |
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".