Pre-existence and emergence of drug resistance in a generalized model of intra-host viral dynamics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Understanding the source of drug resistance emerging within a treated patient is an important problem, from both clinical and basic evolutionary perspectives. Resistant mutants may arise de novo either before or after treatment is initiated, with different implications for prevention. Here we investigate this problem in the context of chronic viral diseases, such as human immunodeficiency virus (HIV) and hepatitis B and C viruses (HBV and HCV). We present a unified model of viral population dynamics within a host, which can capture a variety of viral life cycles. This allows us to identify which results generalize across various viral diseases, and which are sensitive to the particular virus's life cycle. Accurate analytical approximations are derived that allow for a solid understanding of the parameter dependencies in the system. We find that the mutation-selection balance attained prior to treatment depends on the step at which mutations occur and the viral trait that incurs the cost of resistance. Life cycle effects and key parameters, including mutation rate, infected cell death rate, cost of resistance, and drug efficacy, play a role in determining when mutations arising during treatment are important relative to those pre-existing.
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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 | 0.001 | 0.001 |
| 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