Effect of coinfection with hepatitis C virus on survival of individuals with HIV-1 infection
Bibliographic record
Abstract
PURPOSE OF REVIEW: Hepatitis C virus (HCV) coinfection is a common and an important comorbidity in HIV infection. We review current trends in mortality and the potential for early combination antiretroviral therapy (cART) and HCV therapy to improve survival in coinfected patients. RECENT FINDINGS: HIV/HCV coinfection increases risk of death from all causes, and from liver disease and harmful drug use in particular. There is growing evidence for a direct role of HIV in liver fibrogenesis and for cART to decrease the risk of dying from liver disease in coinfected persons. Sustained virologic responses after HCV treatment greatly impact mortality by reducing rates of hepatic decompensation, hepatocellular carcinoma and death from liver-related and nonliver-related causes by at least 50%, but treatment uptake has been low so far. Recent epidemiologic studies do suggest that liver-related mortality is declining in recent calendar periods; however, methodological limitations of currently available studies are important. SUMMARY: Early cART and wider HCV treatment have the potential to markedly reduce HCV-related mortality and thus increase survival overall for HIV-infected populations. However, HCV treatment will need to be greatly scaled up. Given the complex nature of the populations affected, future studies will need to be carefully designed and controlled to rigorously evaluate the impact of these revolutionary therapies on survival.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.001 |
| 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".