Prevalence of Transmitted HIV-1 Drug Resistance and the Role of Resistance Algorithms
Bibliographic record
Abstract
OBJECTIVES: To examine factors influencing the rate of transmitted drug resistance (TDR) among seroconverters, with particular emphasis on 3 widely used genotypic drug resistance algorithms. METHODS: The study used data from CASCADE (Concerted Action on Seroconversion to AIDS and Death in Europe), a collaboration of seroconverter cohorts in Europe and Canada. Genotypic resistance data were derived within 18 months of the last seronegative test or date of laboratory evidence of acute infection and before the initiation of antiretroviral therapy. The Stanford algorithm was used to analyze each individual's nucleotide sequence. A multivariate logistic model was used to assess independent relationships between the presence of TDR and exposure category, sex, age at seroconversion, and year of seroconversion. The paper also describes 3 alternative definitions of resistance: the Stanford algorithm, the key resistance mutations defined by the International AIDS Society, and the Agence Nationale de Recherches sur le Sida (ANRS) algorithm. RESULTS: Forty-five of 438 patients (10.3%) seroconverting between 1987 and 2003 were infected with a drug-resistant HIV-1 variant. Forty patients (9.1%) showed resistance mutations to only 1 class of antiretroviral drugs, 2 (0.5%) to 2 classes, and 3 (0.7%) to 3 classes of antiretroviral therapy. It was suggested that individuals seroconverting later in calendar time were more likely to have TDR (relative risk 3.89 and 95% CI: 0.84 to 18.02, and relative risk 4.69 and 95% CI: 1.03 to 21.31, for 1996-1999 and 2000-2003, respectively, compared with pre-1996; P trend = 0.08). This trend was apparent regardless of the definition of TDR used. The total estimated proportion of individuals with TDR varied between 10.3% and 15.5% according to which definition was used. CONCLUSIONS: Evidence was found for the rise of TDR over time. A specific definition of what constitutes TDR rather than a simple list of mutations is needed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".