Impact of hepatitis B and C co-infection on health-related quality of life in HIV positive individuals
Bibliographic record
Abstract
Purpose: Concurrent infection with HIV, hepatitis B virus (HBV), and hepatitis C virus (HCV) often occurs due to the commonality in risk factors for acquisition. Few studies have examined the effect of co-infection on health-related quality of life (HRQOL) in HIV positive individuals. Methods: Ontario HIV Treatment Network Cohort Study (OCS) participants who completed an annual interviewer-administered questionnaire on up to three occasions were included. Generalized estimating equations (GEE) were used to assess the impact of HBV and HCV co-infection on physical and mental HRQOL component summary scores (range 0-100) as measured by the Medical Outcomes SF-36 health survey. Results: As of March 2010, 1,223 participants had completed the questionnaire; 964 were HIV mono-infected, 128 were HIV-HBV co-infected, 112 were HIV-HCV co-infected, and 19 were HIV-HBV-HCV tri-infected. Eighty percent were male, median age 46 (IQR 40-53) years, 61% Caucasian, median CD4 count 464 (IQR 319-636) cells/mm(3), and 74% had undetectable HIV viremia. Physical HRQOL was lower in HIV-HBV and HIV-HCV co-infected individuals (49.4 (IQR 42.0-53.9) and 48.1 (IQR 36.9-52.8) vs. 51.5 (IQR 45.0-55.4); p = 0.01 and <0.0001) compared to mono-infected individuals. In the multivariable GEE model, the negative impact of HCV remained significant (-2.18; p = 0.01) after adjusting for drug use, smoking, age, and gender. Unadjusted mental HRQOL was lower in HIV-HCV co-infected individuals (44.6 (IQR 34.6-54.0) vs. 48.9 (IQR 36.8-55.9); p = 0.03) compared to mono-infected individuals but no association of mental HRQOL with either co-infection was observed in multivariable GEE models. Conclusions: HCV appears to negatively impact physical HRQOL suggesting a greater health burden for co-infected individuals. HBV and HCV co-infections were not related to lower mental HRQOL among people living with HIV/AIDS.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".