Drug Interactions Between Psychoactive Substances and Antiretroviral Therapy in Individuals Infected With Human Immunodeficiency and Hepatitis Viruses
Bibliographic record
Abstract
The liver disease characteristic of alcohol dependence encompasses three main related entities: steatosis, alcoholic hepatitis, and cirrhosis. Alcoholic cirrhosis is a leading cause of global morbidity and mortality. Alcohol intake among injecting drug users is a major contributor to transmission of viral infections, such as human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C viruses (HCV). HIV and HCV coinfected patients develop liver diseases earlier and more severely than the monoinfected individuals, including hepatocellular carcinoma. Interactions exist between the therapeutic drugs used to minimize and control the drug and alcohol dependence. Furthermore, drug-drug interactions occur between the highly active antiretroviral therapy (HAART) and alcohol, different HAART components and methadone, or each one of the therapies with the other, thus contributing to a higher toxicity level. With the evolution of effective antiretroviral therapy, survival of persons with HIV, and the syndrome it causes, acquired immunodeficiency syndrome (AIDS) has increased dramatically. Drug-drug interactions may appear between alcohol and anti-HBV or anti-HCV, therapy in the presence or absence of anti-HIV therapy. Several other medical-, social-, and drug-related factors of this population have to be considered when providing HAART. Because many coinfected patients also have problems with substance use, dealing with their drug dependence is an important first step in an attempt to improve adherence to and tolerance of antiviral therapy. It is necessary to minimize the risk of liver disease acceleration and/or reinfection with hepatitis viruses. Knowledge of potential drug interactions between methadone, antiretroviral therapy, psychoactive drugs, and antipsychotics and the role of coinfection with HBV or HCV and the drugs used in eradicating viral hepatitis permits suitable antiretroviral combinations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".