Hepatitis B or hepatitis C coinfection in HIV‐infected pregnant women in Europe
Bibliographic record
Abstract
OBJECTIVES: The aim of the study was to investigate the prevalence of and risk factors for hepatitis C or B virus (HCV or HBV) coinfection among HIV-infected pregnant women, and to investigate their immunological and virological characteristics and antiretroviral therapy use. METHODS: Information on HBV surface antigen (HBsAg) positivity and HCV antibody (anti-HCV) was collected retrospectively from the antenatal records of HIV-infected women enrolled in the European Collaborative Study and linked to prospectively collected data. RESULTS: Of 1050 women, 4.9% [95% confidence interval (CI) 3.6-6.3] were HBsAg positive and 12.3% (95% CI 10.4-14.4) had anti-HCV antibody. Women with an injecting drug use(r) (IDU) history had the highest HCV-seropositivity prevalence (28%; 95% CI 22.8-35.7). Risk factors for HCV seropositivity included IDU history [adjusted odds ratio (AOR) 2.92; 95% CI 1.86-4.58], age (for > or =35 years vs. <25 years, AOR 3.45; 95% CI 1.66-7.20) and HBsAg carriage (AOR 5.80; 95% CI 2.78-12.1). HBsAg positivity was associated with African origin (AOR 2.74; 95% CI 1.20-6.26) and HCV seropositivity (AOR 6.44; 95% CI 3.08-13.5). Highly active antiretroviral therapy (HAART) use was less likely in HIV/HCV-seropositive than in HIV-monoinfected women (AOR 0.34; 95% CI 0.20-0.58). HCV seropositivity was associated with a higher adjusted HIV RNA level (+0.28 log(10) HIV-1 RNA copies/mL vs. HIV-monoinfected women; P=0.03). HIV/HCV-seropositive women were twice as likely to have detectable HIV in the third trimester/delivery as HIV-monoinfected women (AOR 1.95; P=0.049). CONCLUSIONS: Although HCV serostatus impacted on HAART use, the association between HCV seropositivity and uncontrolled HIV viraemia in late pregnancy was independent of HAART.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".