Adherence–resistance relationships for protease and non-nucleoside reverse transcriptase inhibitors explained by virological fitness
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To compare the prevalence of resistance by adherence level in patients treated with non-nucleoside reverse transcriptase inhibitors (NNRTI) or protease inhibitors (PI). Also to examine the mechanism of differential class-specific adherence-resistance relationships, focusing on the patient-derived capacity of wild-type and drug-resistant recombinant variants to replicate in vitro in the presence of variable drug levels. METHODS: Participants received unannounced pill count measures to assess adherence, viral load monitoring, and genotypic resistance testing. The replicative capacity of drug-susceptible and drug-resistant recombinants was determined using a single-cycle recombinant phenotypic susceptibility assay. Drug exposure was estimated using population-averaged pharmacological measurements adjusted by participant-specific levels of adherence. RESULTS: In the NNRTI-treated group, 69% had resistance at 0-48% adherence compared to 13% at 95-100% (P = 0.01). PI resistance was less common than NNRTI resistance at 0-48% adherence (69% versus 23%; P = 0.01). In multivariate analysis, the odds for PI resistance increased (P = 0.03) while the odds for NNRTI resistance decreased (P = 0.04) with improving adherence. Individuals with drug-resistant variants were more likely to have levels of drug exposure where the resistant variant was more fit than the drug-susceptible variant in vitro, while those with drug-susceptible virus were more likely to have levels of drug exposure where the drug-susceptible virus was more fit than the drug-resistant variant (P = 0.005). CONCLUSIONS: NNRTI resistance was more common than PI resistance at low levels of adherence. Class-specific adherence-resistance relationships are associated with the relative replicative capacity of drug-resistant versus wild-type variants to replicate in the presence of clinically relevant drug levels.
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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