Determination of Clinically Relevant Cutoffs for HIV-1 Phenotypic Resistance Estimates Through a Combined Analysis of Clinical Trial and Cohort Data
Bibliographic record
Abstract
BACKGROUND: Clinically relevant cutoffs are needed for the interpretation of HIV-1 phenotypic resistance estimates as predicted by "virtual" phenotype HIV resistance analysis. METHODS: Using a clinical data set containing 2596 treatment change episodes in 2217 patients in 8 clinical trials and 2 population-based cohorts, drug-specific linear regression models were developed to describe the relation between baseline characteristics (resistance, viral load, and treatment history), new treatment regimen selected, and 8-week virologic outcome. RESULTS: These models were used to derive clinical cutoffs (CCOs) for 6 nucleoside/nucleotide reverse transcriptase inhibitors (zidovudine, lamivudine, stavudine, didanosine, abacavir, and tenofovir), 3 unboosted protease inhibitors (PIs; indinavir, amprenavir, and nelfinavir), and 4 ritonavir-boosted PIs (indinavir/ritonavir, amprenavir/ritonavir, saquinavir/ritonavir, lopinavir/ritonavir). The CCOs were defined as the phenotypic resistance levels (fold change [FC]) associated with a 20% and 80% loss of predicted wild-type drug effect and depended on the drug-specific dynamic range of the assay. CONCLUSIONS: The proposed CCOs were better correlated with virologic response than were biological cutoffs and provide a relevant tool for estimating the resistance to antiretroviral drug combinations used in clinical practice. They can be applied to diverse patient populations and are based on a consistent methodologic approach to interpreting phenotypic drug 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 | 0.149 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".