Relationship of Chronic Hepatitis C Infection to Rates of AIDS-Defining Illnesses in a Canadian Cohort of HIV Seropositive Individuals Receiving Highly Active Antiretroviral Therapy
Bibliographic record
Abstract
BACKGROUND: The influence of chronic hepatitis C virus (HCV) infection on the risk, timing, and type of AIDS-defining illnesses (ADIs) is not well described. To this end, rates of ADIs were evaluated in a Canadian cohort of HIV seropositive individuals receiving highly active antiretroviral therapy (HAART). METHODS: ADIs were classified into 6 Centers for Disease Control and Prevention (CDC)-defined etiological subgroups: non-Hodgkin lymphoma, viral infection, bacterial infection, HIV-related disease, protozoal infection, and mycotic infection. Generalized estimating equation (GEE) Poisson regression models were used to estimate the effect of HCV on rates of ADIs after adjusting for covariates. RESULTS: Among 2,706 HAART recipients, 768 (28%) were HCV coinfected. Rates of all ADIs combined and of bacterial infection, HIV-related disease, and mycotic infection were increased in HCV-coinfected persons and among those with CD4 counts <200 cells/mm3 HCV was associated with an increased risk of ADIs (rate ratio [RR], 1.38; 95% CI, 1.01-1.88) and a 2-fold increased risk of mycotic infections (RR, 2.21; 95% CI, 1.35-3.62) in univariate analyses and after adjusting for age, baseline viral load, baseline CD4 count, and region of Canada. However, after further adjustment for HAART interruptions, HCV was no longer associated with an increased rate of ADIs overall (RR, 1.13; 95% CI, 0.80-1.59), but remained associated with an increased rate of mycotic infections (RR, 1.97, 95% CI, 1.08-3.61). CONCLUSION: Although HCV coin-fected individuals are at increased risk of developing ADIs overall, our analysis suggests that behavioral variables associated with HCV (including rates of retention on HAART), and not biological interactions with HCV itself, are primarily responsible.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".