HIV‐1 Drug Resistance: Degree of Underestimation by a Cross‐Sectional versus a Longitudinal Testing Approach
Bibliographic record
Abstract
Genotyping of human immunodeficiency virus type 1 (HIV-1) for antiretroviral drug resistance is routinely used both in clinical practice, to guide the selection of options for an individual's antiretroviral therapy, and in epidemiological studies, to estimate levels of antiretroviral drug resistance in a patient population. However, reliance on results of a single test can result in an underestimation of antiretroviral drug resistance. In the present study, we quantified the prevalence of resistance-associated mutations found in recent genotypic tests of 1734 HIV-1-infected, treatment-experienced subjects who had at least 3 genotypic tests (n = 11,404 genotypic tests total; median, 5 tests/subject) and compared it with that of resistance-associated mutations ever detected in these subjects between 1996 and 2004. Single-point analyses underestimated antiretroviral drug resistance, particularly for nucleoside analogues, in both individuals and patient populations. For example, the prevalence of resistance-associated mutation M184V/I was 25.5% in the most recent genotypes and 58.8% in available historical genotypes. Our results suggest that analysis of a combined historical genotype rather than of a cross-sectional genotype may lead to more accurate estimates of antiretroviral drug resistance in individual patients and in patient populations.
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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 | 0.061 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".