Epidemiology of Antiretroviral Multiclass Resistance
Bibliographic record
Abstract
Given the recent evolution of therapeutic trends, the frequency and determinants of multiclass-resistant HIV infection in the modern combination highly active antiretroviral therapy (HAART) era are less well understood. In this study, the authors characterize the epidemiology of antiretroviral multiclass resistance among HAART-naïve patients enrolled in a province-wide HAART distribution program in British Columbia, Canada. HAART and resistance testing are free to eligible individuals in British Columbia. This study was based on patients who initiated naïve on HAART and were followed during January 1, 2000-June 30, 2007. Explanatory logistic and survival models were built to identify those factors most influential in the emergence of multiclass resistance. Among the 1,820 individuals in our study, 833 (46%) were tested for antiretroviral resistance at least once during their follow-up. Multiclass resistance was observed in 142 individuals (n = 833; 17%) during a median follow-up of 14 months (interquartile range, 3-34 months) (incidence rate, 0.8 cases/1,000 person-months). The authors found that initial nonnucleoside reverse transcriptase inhibitor-based HAART was the main determinant of multiclass resistance. Given that these inhibitors are still widely used, priority should be given to make resistance testing and viral load monitoring a standard part of human immunodeficiency virus care to maximize the long-term efficacy and efficiency of HAART.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".