Alcohol-Associated Liver Disease: Integrated Management With Alcohol Use Disorder
Bibliographic record
Abstract
Alcohol-associated liver disease (ALD) is the most common cause of cirrhosis and liver-related mortality in many regions worldwide. Around 75% of patients with cirrhosis are unaware of their disease until they are referred to the emergency department. An innovative, noninvasive screening approach is required for an earlier diagnosis of liver fibrosis. In patients with ALD the physician is inevitably dealing with 2 major disorders: the liver disease itself and the alcohol use disorder (AUD). Focus only on the liver disease will inevitably lead to failure because transient improvements in liver function are rapidly overturned if the patient returns to alcohol consumption. For this reason, integrated models of care provided by hepatologists and addiction specialists are an effective approach, which are, however, not widely available. There are multiple pharmacologic and non-pharmacologic therapies for AUD. Progress has recently been made in the management of patients with severe AH who have improved survival through better understanding of the concept of response to medical treatment, improved survival prediction, and the advent of early liver transplantation. The emerging concept is that listing for transplantation a patient with severe ALD could lead to adjusting the duration of abstinence according to the severity and evolution of liver dysfunction and the patient's addictive profile.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.004 | 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".