Risk of Viral Failure Declines With Duration of Suppression on Highly Active Antiretroviral Therapy Irrespective of Adherence Level
Bibliographic record
Abstract
OBJECTIVE: To model the effect of adherence and duration of viral suppression on the risk of viral rebound. METHODS: Viral rebound was defined as the first of at least two consecutive viral loads greater than 400 copies/mL after initial viral suppression. The main exposures were adherence, presence of antiretroviral class resistance before rebound or censoring date, and the percentage of follow-up time with viral suppression. RESULTS: A total of 274 (N = 1305 [21%]) individuals experienced viral rebound. Median time of suppression before rebound was 2 years. Viral rebound was less likely to occur among those with longer duration of continuous viral suppression (odds ratio, 0.37; 95% confidence interval, 0.32 to 0.42). Among individuals with moderate levels of adherence (80% to less than 95%), the probability of virologic failure was 0.85 after being suppressed for 12 months and it was 0.08 after 72 months being suppressed (P < 0.01). Individuals with drug resistance were at a higher risk of viral rebound. CONCLUSIONS: The risk of viral rebound decreased with longer duration of viral suppression within each of adherence strata studied. Although perfect adherence remains an important goal of therapy to prevent disease progression, individuals with long-term viral suppression may be able to miss more doses without experiencing viral rebound.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".