Bibliographic record
Abstract
Alcohol misuse is a risk factor for the development of drug-induced liver injury (DILI) in patients taking medication for several diseases. Our purpose is to review the effects of recreational drugs and therapeutics combined with alcohol consumption, in determining hepatocytotoxicity or influencing the evolution of chronic diseases of the liver, specifically non-alcoholic fatty liver disease (NAFLD). A deleterious role of daily use of recreational drugs, in particularly cannabis, has been shown to demonstrate clearly a rapid progression of fibrosis and steatosis, leading to increase severity of liver injury in patients taking medication. The effects of the misuse of substances of misuse on NAFLD, the main obesity-related comorbidity, leading to addiction, need to be elucidated in order to prevent hepatotoxicity. We will present a case of 0 alcohol consumption and medical cannabis prescribed for Tourette syndrome that led to acute liver failure. Also, a thalassemic patient that was prescribed iron chelators and misused alcohol was diagnosed with drug-induced liver injury (DILI). The chronic consumption of alcohol provides a significant risk factor for the development of cirrhosis. Alcohol misuse and over the counter drugs (sulphonamides, or anti-inflammatory) or antiepileptic medication may lead to DILI and liver transplant. This presentation aims at raising awareness about this topic.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 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".