Explaining variability in the relationship between antiretroviral adherence and HIV mutation accumulation
Bibliographic record
Abstract
OBJECTIVES: Determining the relationship between antiretroviral adherence and resistance accumulation is important for the design and evaluation of adherence interventions. Our objective was to explain heterogeneity observed in this relationship. METHODS: We first conducted a systematic review to locate published reports describing the relationship between adherence and resistance. We then used a validated computer simulation to simulate the patient populations in these reports, exploring the impact of changes in individual patient characteristics (age, CD4, viral load, prior antiretroviral experience) on the shape of the adherence-resistance (A-R) curve. RESULTS: The search identified 493 titles, of which 3 contained relevant primary data and 2 had sufficient follow-up for inclusion (HOMER and REACH cohorts). When simulating HOMER, the A-R curve had a high peak with a greatly increased hazard ratio (HR) of accumulating mutations at partial compared to complete adherence (simulation, HR 2.9; HOMER, HR 2.7). When simulating REACH, the A-R curve had a shallow peak with a slightly increased hazard of accumulating mutations at partial adherence (simulation, HR 1.2; REACH, HR 1.4). This heterogeneity was primarily attributable to differences in antiretroviral experience between the cohorts. CONCLUSIONS: Our computer simulation was able to explain much of the heterogeneity in observed A-R curves.
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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 | 0.043 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".