Clinical and demographic predictors of antiretroviral efficacy in HIV–HBV co-infected patients
Bibliographic record
Abstract
Background: The clinical and demographic characteristics that predict antiretroviral efficacy among patients co-infected with HIV and hepatitis B virus (HBV) remain poorly defined. We evaluated HIV virological suppression and rebound in a cohort of HIV-HBV co-infected patients initiated on antiretroviral therapy. Methods: A retrospective cohort analysis was performed with Canadian Observation Cohort Collaboration data. Cox proportional hazards models were used to determine the factors associated with time to virological suppression and time to virological rebound. Results: HBV status was available for 2,419 participants. A total of 8% were HBV co-infected, of whom 95% achieved virological suppression. After virological suppression, 29% of HIV-HBV co-infected participants experienced HIV virological rebound. HBV co-infection itself did not predict virological suppression or rebound risk. The rate of virological suppression was lower among patients with a history of injection drug use or baseline CD4 cell counts of <199 cells per cubic millimetre. Low baseline HIV RNA and men-who-have-sex-with-men status were significantly associated with a higher rate of virological suppression. Injection drug use and non-White race predicted viral rebound. Conclusions: HBV co-infected HIV patients achieve similar antiretroviral outcomes as those living with HIV mono-infection. Equitable treatment outcomes may be approached by targeting resources to key subpopulations living with HIV-HBV co-infection.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".