Predictors of unstructured antiretroviral treatment interruption and resumption among <scp>HIV</scp>‐positive individuals in <scp>C</scp>anada
Bibliographic record
Abstract
OBJECTIVES: Sustained optimal use of combination antiretroviral therapy (cART) has been shown to decrease morbidity, mortality and HIV transmission. However, incomplete adherence and treatment interruption (TI) remain challenges to the full realization of the promise of cART. We estimated trends and predictors of treatment interruption and resumption among individuals in the Canadian Observational Cohort (CANOC) collaboration. METHODS: cART-naïve individuals ≥ 18 years of age who initiated cART between 2000 and 2011 were included in the study. We defined TIs as ≥ 90 consecutive days off cART. We used descriptive analyses to study TI trends over time and Cox regression to identify factors predicting time to first TI and time to treatment resumption after a first TI. RESULTS: A total of 7633 participants were eligible for inclusion in the study, of whom 1860 (24.5%) experienced a TI. The prevalence of TI in the first calendar year of cART decreased by half over the study period. Our analyses highlighted a higher risk of TI among women [adjusted hazard ratio (aHR) 1.59; 95% confidence interval (CI) 1.33-1.92], younger individuals (aHR 1.27; 95% CI 1.15-1.37 per decade increase), earlier treatment initiators (CD4 count ≥ 350 vs. <200 cells/μL: aHR 1.46; 95% CI 1.17-1.81), Aboriginal participants (aHR 1.67; 95% CI 1.27-2.20), injecting drug users (aHR 1.43; 95% CI 1.09-1.89) and users of zidovudine vs. tenofovir in the initial cART regimen (aHR 2.47; 95% CI 1.92-3.20). Conversely, factors predicting treatment resumption were male sex, older age, and a CD4 cell count <200 cells/μL at cART initiation. CONCLUSIONS: Despite significant improvements in cART since its advent, our results demonstrate that TIs remain relatively prevalent. Strategies to support continuous HIV treatment are needed to maximize the benefits of cART.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".