The Relationship Between Resistance and Adherence in Drug-Naive Individuals Initiating HAART Is Specific to Individual Drug Classes
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship between HIV-1 drug resistance and adherence and the accumulation rate of resistance mutations in 1191 HIV-infected, antiretroviral-naive adults initiating highly active antiretroviral therapy in British Columbia, Canada. METHODS: Plasma samples with plasma viral load >1,000 copies per milliliter collected within 30 months of follow-up were genotyped for drug resistance. Adherence was estimated using prescription refills and plasma drug levels. The primary outcome measure was time to detection of drug resistance. Cox proportional hazard regression was used to calculate hazard ratios (HRs) associated with baseline variables. RESULTS: The accumulation rates of multiple primary and secondary mutations were similar in patients initiating highly active antiretroviral therapy with protease inhibitor versus nonnucleoside reverse transcriptase inhibitor (NNRTI). Rates decreased approximately 50% per additional mutation. At 80%-90% adherence based on refills, there was greater risk of detecting lamivudine (3TC) [HR 3.0, 95% confidence interval (CI): 1.9 to 4.7; P < 0.0001] and NNRTI mutations (HR 6.0, 95% CI: 3.3 to 10.9; P < 0.0001) compared with the >or=95% refill reference group. In a multivariate model, individuals with <95% refills and consistently detectable plasma drug levels were at increased risk for 3TC (HR 4.5, 95% CI: 2.6 to 7.9; P = 0.0001) and NNRTI resistance (HR 7.0, 95% CI: 3.4 to 14.5; P = 0.0001) compared with the reference group of >or=95% refills with consistently detectable drug levels. Adherence-resistance relationships were much weaker for protease inhibitors and nucleoside reverse transcriptase inhibitors as there was little variance in HRs among the different adherence strata compared with 3TC and NNRTIs. CONCLUSION: The relationships between resistance, adherence, and mutation accumulation differ between HIV drug classes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".