Increased Resilience to the Development of Drug Resistance with Modern Boosted Protease Inhibitor–Based Highly Active Antiretroviral Therapy
Bibliographic record
Abstract
BACKGROUND: We explore the temporal and regimen-specific changes of HIV-1 drug resistance in a large cohort of antiretroviral-naive individuals starting highly active antiretroviral therapy (HAART). METHODS: Individuals (n = 2350) initiating first HAART between August 1996 and November 2004 were followed until November 2005 (median follow-up, 4.8 years; n = 6066 tests). A logistic regression model was used to predict the probability of the emergence of resistance, adjusting for baseline predictors. RESULTS: The cohort included 991 individuals initiating nonboosted protease inhibitor (PI)-based regimens, 475 initiating ritonavir-boosted PI-based regimens, and 884 initiating nonnucleoside reverse-transcriptase inhibitor (NNRTI)-based regimens. There was no difference in the development of resistance between nonboosted PI-based regimens (reference group) and NNRTI-based HAART regimens (odds ratio [OR], 1.09 [95% confidence interval {CI}, 0.84-1.42]), but there were greatly reduced odds for boosted PI-based regimens (OR, 0.42 [95% CI, 0.28-0.62]). Individuals initiating HAART more recently (2002-2004) were at a reduced risk of resistance, compared with those who started HAART between 1996 and 1998 (OR, 0.43 [95% CI, 0.30-0.61]). CONCLUSIONS: Individuals initiating first HAART with a boosted PI-based regimen had a 2.4-fold lower OR for developing HIV drug resistance than did those starting nonboosted PI-based or NNRTI-based HAART, at all adherence levels. The data demonstrate marked temporal improvement in the likelihood of the development of drug resistance for those initiating more recent HAART regimens.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".