The Impact of Hepatitis C Virus Coinfection on HIV Progression Before and After Highly Active Antiretroviral Therapy
Bibliographic record
Abstract
To compare the impact of hepatitis C virus (HCV) coinfection on progression of HIV infection in the eras before and after the introduction of highly active antiretroviral therapy (HAART), the authors conducted a retrospective cohort study. One hundred twenty-five HCV+ patients and 1076 HCV- patients were studied; 83% of HCV+ patients were injection drug users. HCV+ subjects experienced no clear benefit from HAART. The adjusted hazard ratios (HRs) of opportunistic infection, death, and hospitalization were 0.74 (95% CI: 0.31-1.78), 1.78 (95% CI: 0.59-5.37), and 2.1 (95% CI: 0.90-4.90), respectively, comparing the post-HAART era with the pre-HAART era. In contrast, HCV- subjects experienced rate reductions for all outcomes. Comparable HRs for opportunistic infection, death, and hospitalization were 0.49 (95% CI: 0.37-0.64), 0.28 (95% CI: 0.19-0.41), and 0.51 (95% CI: 0.38-0.67), respectively. HCV+ subjects remained at increased risk for death and hospitalization post-HAART even after additional adjustment for antiretroviral use and time-updated CD4 cell and viral load measures. Deaths and hospitalizations in HCV+ patients were primarily for non-AIDS-defining infections and complications of injection drug use. HCV coinfection and comorbidity associated with injection drug use are preventing the realization of substantial health benefits associated with HAART.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".