Insights Into the Coinfections of Human Immunodeficiency Virus-Hepatitis B Virus, Human Immunodeficiency Virus-Hepatitis C Virus, and Hepatitis B Virus-Hepatitis C Virus: Prevalence, Risk Factors, Pathogenesis, Diagnosis, and Treatment
Bibliographic record
Abstract
Human immunodeficiency virus, hepatitis B virus, and hepatitis C virus are three blood-borne viruses that can cause major global health issues by increasing severe morbidity. There is a high risk of coinfection with these viruses in individuals because of their same transmission routes through blood using shared needles, syringes, other injection equipment, sexual transmission, or even vertical transmission. Coinfection can cause various liver-related illnesses, non-hepatic organ dysfunction, followed by death compared to any of these single infections. The treatment of coinfected patients is complicated due to the side effects of antiviral medication, resulting in drug resistance, hepatotoxicity, and a lack of required responses. On the other hand, coinfected individuals must be treated with multiple drugs simultaneously, such as for HIV either along with HBV or HCV and HBV and HCV. Therefore, diagnosing, treating, and controlling dual infections with HIV, HBV, or HCV is complicated and needs further investigation. This review focuses on the current prevalence, risk factors, and pathogenesis of dual infections with HIV, HBV, and HCV. We also briefly overviewed the diagnosis and treatment of coinfections of these three blood-borne viruses.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".