Assessing the effectiveness of antiretroviral regimens in cohort studies involving HIV-positive injection drug users
Bibliographic record
Abstract
OBJECTIVE: We compared the effectiveness of different highly active antiretroviral therapy (HAART) regimens considering, separately, history of injection drug use (IDU) (yes/no). DESIGN, METHODS: A total of 1163 HIV-infected patients initiated HAART between 1 January 2000 and 28 February 2009 in British Columbia, Canada, and were followed until 28 February 2010. HAART effectiveness was measured by the ability to achieve viral suppression below 50 copies/ml at 6 months. We compared HAART regimens containing efavirenz and boosted atazanavir. We developed logistic regression models using different techniques to control for potential confounders. RESULTS: Among the 1163 patients, 796 (68%) achieved viral suppression at 6 months (32% reporting a history of IDU). Different confounding models yielded very similar odds ratios for achieving viral suppression. Boosted atazanavir-based HAART demonstrated to be the most effective regimen, showing a surprisingly higher benefit for patients with a history of IDU (odds ratios from different models ranged from 1.74-1.95 to 1.45-1.51). CONCLUSIONS: The literature has conflicting results regarding the effectiveness of HAART to treat HIV infection among those with a history of IDU. We have shown that most patients, with and without a history of IDU, were able to achieve viral suppression at 6 months. Boosted atazanavir-based HAART was the most resilient regimen and it was more effective than efavirenz-based HAART among IDUs. Given the limited inclusion of IDU in clinical trials of HAART's efficacy, a randomized clinical trial comparing different first-line HAART regimens among IDU is warranted based on these results.
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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 | 0.068 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".