Predictors of HIV Drug‐Resistance Mutations in a Large Antiretroviral‐Naive Cohort Initiating Triple Antiretroviral Therapy
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to systematically characterize the incidence and determinants of antiretroviral resistance in the HOMER (Highly Active Antiretroviral Therapy [HAART] Observational Medical Evaluation and Research) cohort of 1191 human immunodeficiency virus-infected, antiretroviral-naive adults initiating HAART in British Columbia, Canada. METHODS: All plasma samples with plasma virus loads (pVLs) >1000 copies/mL collected during the first 30 months of follow-up were genotyped for drug resistance. The primary outcome measure was time to the first detection of major drug-resistance mutation(s). Cox proportional hazard regression was used to identify factors significantly associated with the detection of drug-resistance mutations. RESULTS: Drug-resistance mutations were detected in 298 subjects (25%). Factors significantly associated with detection of drug-resistance mutations included high baseline pVL (multivariate hazard ratio [HR], 1.59; P<.001) and adherence (estimated using prescription-refill data and/or untimed plasma drug-concentration measurements). When compared with subjects with low (0%-<20%) prescription-refill percentages, subjects at an elevated risk of harboring drug-resistance mutations were those with relatively high but imperfect prescription-refill percentages (80%-<90%; multivariate HR, 4.15; P<.001) and those with essentially perfect (>/=95%) refill percentages but with 2 plasma drug concentrations below the steady-state trough concentration minus 1 standard deviation (multivariate HR, 4.57; P<.001). Initial use of nonnucleoside reverse-transcriptase inhibitor-based HAART was significantly associated with multiclass drug resistance (multivariate HR, 1.84; P=.001). CONCLUSION: High baseline pVLs and substantial but imperfect levels of adherence were major predictors of antiretroviral resistance.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".