Bibliographic record
Abstract
The importance of treating hepatitis C virus (HCV)-associated morbidities in a growing population of patients coinfected with human immunodeficiency virus (HIV) has increased since the introduction of highly active antiretroviral therapy. As a result, investigative attention is turning to HCV-related liver disease and treatment-associated issues in coinfection. HIV/HCV-coinfected patients have higher HCV RNA loads and show more rapid progression of fibrosis than do monoinfected patients. Combination therapy with pegylated interferon plus ribavirin (RBV) is the standard of care for HCV in coinfected patients. Therapy slows fibrosis progression, but toxicity prevents identification of the most effective RBV dose. Coinfected patients have about a threefold greater risk of antiretroviral therapy-associated hepatotoxicity than patients with HIV only. Other challenges include anaemia, mitochondrial toxicity, drug-drug interactions and leucopenia. Thus, chronic hepatitis C should be treated in HIV/HCV-coinfected patients, but steps must be taken to prevent and treat potential toxicities. The first European Consensus Conference on the Treatment of Chronic Hepatitis B and C in HIV Co-infected Patients was held March 2005 in Paris to address these issues. This article reviews the peer-reviewed literature and expert opinion published from 1990 to 2005, and compares results with presentations and recommendations from the Consensus Conference to best present current issues in coinfection.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".