Effect of incident hepatitis C infection on CD4+ cell count and HIV RNA trajectories based on a multinational HIV seroconversion cohort
Bibliographic record
Abstract
BACKGROUND: Most studies on hepatitis C virus (HCV)/HIV-coinfection do not account for the order and duration of these two infections. We aimed to assess the effect of incident HCV infection, and its timing relative to HIV seroconversion (HIVsc) in HIV-positive MSM on their subsequent CD4+ T-cell count and HIV RNA viral load trajectories. METHODS: We included MSM with well estimated dates of HIVsc from 17 cohorts within the CASCADE Collaboration. HCV-coinfected MSM were matched to as many HIV monoinfected MSM as possible by HIV-infection duration and combination antiretroviral therapy (cART) use. We used multilevel random-effects models stratified by cART use to assess differences in CD4+ cell count and HIV RNA viral load trajectories by HCV-coinfection status. FINDINGS: We matched 214 (ART-naive) and 147 (on cART) HCV-coinfected MSM to 5384 and 3954, respectively, matched controls. The timing of HCV seroconversion (HCVsc) relative to HIVsc had no demonstrable effect on HIV RNA viral load or CD4+ cell count trajectories. In the first 2-3 years following HCVsc CD4 cell counts were lower among HCV-coinfected MSM, but became comparable with HIV monoinfected MSM thereafter. In ART-naive MSM, during the first 2 years after HCVsc, HIV RNA viral load levels were lower or comparable with HIV monoinfected, tending to be higher thereafter. In MSM on cART, HCV had no significant effect on having a detectable HIV RNA viral load. INTERPRETATION: Irrespective of the duration of HIV infection when HCV is acquired, CD4+ cell counts were temporarily lower following HCVsc, even when on cART. The clinical implications of our findings remain to be further elucidated.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".