Predictors of HIV Drug‐Resistance Mutations in a Large Antiretroviral‐Naive Cohort Initiating Triple Antiretroviral Therapy
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it