Comparison of first antiretroviral treatment duration and outcome in HIV, HIV–HBV and HIV–HCV infection
Bibliographic record
Abstract
Hepatitis C virus (HCV) and hepatitis B virus (HBV) co-infection may differentially influence HIV treatment duration and outcome. This was assessed at The Ottawa Hospital Immunodeficiency Clinic in first-time highly active antiretroviral therapy (HAART) recipients visited between January 2000 and December 2004. Of 968 patients, 526/700 (75%) HIV, 173/230 (75%) HIV-HCV and 30/38 (79%) HIV-HBV-infected patients initiated HAART. Co-infected patients stopped treatment sooner (HBV - 10 months, HCV - 9 months) than HIV mono-infected (17 months) (P<0.001). Injection drug history predicted shorter treatment duration (odds ratio [OR]1.59, P<0.001). Use of non-nucleoside-reverse-transcriptase-inhibitor-containing HAART (OR 0.76, P<0.01) and low-dose ritonavir (<400 mg twice daily)-based HAART (OR 0.83, P = 0.06) predicted longer treatment duration. HCV co-infection did not predict duration of therapy (OR 1.19, P=0.19) once controlled for by these three variables. Poor adherence was a major explanation for eventual treatment interruption in those with HIV-HCV (22% versus 5% in HIV alone; P<0.001) as was substance abuse (7% versus < 1% in HIV; P<0.001). Metabolic complications resulted in HAART interruption in HIV mono-infection (8%) but not with HBV or HCV co-infection (both <1%; P<0.001). Antiretroviral selection is critical to the longevity of initially prescribed regimens, irrespective of viral hepatitis co-infection. Attention to this and strategies targeting substance abuse and adherence in HIV-HCV are predicted to increase the duration 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 | 0.002 | 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 | 0.001 |
| 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".