Rates of antiretroviral resistance among HIV-infected patients with and without a history of injection drug use
Bibliographic record
Abstract
BACKGROUND: There exist concerns regarding the potential for elevated rates of antiretroviral resistance among HIV-infected injection drug users (IDUs) prescribed highly active antiretroviral therapy (HAART), however, no population-based study has examined if IDUs have elevated rates of antiretroviral resistance in comparison to non-IDUs. OBJECTIVE: To evaluate the time to the development of antiretroviral resistance among antiretroviral-naive patients with and without a history of injection drug use. METHODS: In British Columbia there is a province-wide HIV/AIDS treatment program that provides antiretrovirals free of charge. We examined all antiretroviral-naive patients initiating HAART between 1 August 1996 and 30 September 2000 and who were followed to 31 March 2002. The main outcome measure was the time to class-specific antiretroviral resistance. Cumulative antiretroviral resistance rates among IDUs and non-IDUs were evaluated using Kaplan-Meier methods and relative hazards were estimated using Cox regression. RESULTS: Overall, 1191 antiretroviral-naive patients initiated HAART during the study period. Resistance mutations were observed in 298 (25%) subjects during the first 30 months of HAART. In comparison with non-IDUs, the risk of protease inhibitor resistance [relative hazard (RH), 0.9; 95% confidence interval (CI), 0.5-1.6] and non-nucleoside reverse transcriptase inhibitor resistance (RH, 1.5; 95% CI, 1.0-2.2) were similar among IDUs, and there were no differences in the rates of resistance to the sub-classes of nucleoside reverse transcriptase inhibitors. CONCLUSIONS: Resistance to all major classes of antiretrovirals were similar among IDUs and non-IDUs after 30 months of follow-up. These findings should help to allay fears that prescribing HAART to IDUs may result in elevated rates of resistance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".