Reporting on the Prevalence of Antiretroviral Drug Resistance in a Regional HIV Population over 20 Years: A Word of Caution
Bibliographic record
Abstract
BACKGROUND: Failure to achieve complete viral suppression with antiretroviral drugs (ARV) may lead to uncontrolled HIV replication, ARV resistance and negative outcomes. Monitoring and reporting of HIV resistance trends is important but problematic. We examined prevalent resistance rates in an HIV population over 20 years and document how rates may appear to vary greatly based solely on which parameters are utilized. METHODS: We determined the annual use of genotypic antiretroviral resistance testing (GART) from 1995 to 2014 for all patients receiving HIV care in southern Alberta, Canada, and the presence of resistance mutations in those tested. The impact on prevalent resistance rates of using cumulative or latest GART was also determined. RESULTS: Between 1995 and 2014, the number of patients with GART increased from <1% to 71%. Prevalent resistance in patients with GART decreased from a high of 52% in 2003 to 25.8% in 2014. However, if prevalence rates were reported using all active patients as denominator, including those without GART, prevalence increased from 0.7% to 18.5%. Prevalence rates were 7% to 9% higher in any given year if cumulative GART rather than latest GART results were used. CONCLUSIONS: While prevalence resistance rates are decreasing, the precise rates being reported may vary due to increasing number of patients tested annually, using either the entire population as denominator or only patients with GART, and using either last or cumulative GART. Defining these parameters is critical if prevalence is to be compared over time or between HIV 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 | 0.158 | 0.353 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.008 | 0.023 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".