“Dynamic Range” of Inferred Phenotypic HIV Drug Resistance Values in Clinical Practice
Bibliographic record
Abstract
BACKGROUND: 'Virtual' or inferred phenotypes (vPhenotypes) are commonly used to assess resistance to antiretroviral agents in patients failing therapy. In this study, we provide a clinical context for understanding vPhenotype values. METHODS: All HIV-infected persons enrolled in the British Columbia Drug Treatment Program with a baseline plasma viral load (pVL) and follow-up genotypic resistance and pVL results were included up to October 29, 2008 (N = 5,277). Change from baseline pVL was determined as a function of Virco vPhenotype, and the "dynamic range" (defined here by the 10th and 90th percentiles for fold-change in IC₅₀ amongst all patients) was estimated from the distribution of vPhenotye fold-changes across the cohort. RESULTS: The distribution of vPhenotypes from a large cohort of HIV patients who have failed therapy are presented for all available antiretroviral agents. A maximum change in IC₅₀ of at least 13-fold was observed for all drugs. The dideoxy drugs, tenofovir and most PIs exhibited small "dynamic ranges" with values of <4-fold change observed in > 99% of samples. In contrast, zidovudine, lamivudine, emtricitabine and the non-nucleoside reverse transcriptase inihibitors (excluding etravirine) had large dynamic ranges. CONCLUSION: We describe the populational distribution of vPhenotypes such that vPhenotype results can be interpreted relative to other patients in a drug-specific manner.
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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 | 0.018 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".