Antiretroviral therapy use, self‐reported adherence, and viral suppression among women living with <scp>HIV</scp> in Canada
Bibliographic record
Abstract
BACKGROUND: Traditionally, ≥95% adherence was recommended for viral suppression (VS). Newer antiretroviral therapy (ART) is now being increasingly prescribed and may be more forgiving of lower adherence. The lifelong nature of ART presents adherence challenges, particularly for women living with HIV. We aimed to describe ART use and examine the association between adherence and VS. METHODS: The Canadian HIV Women's Sexual and Reproductive Health Cohort, which included 1422 participants, was used. Data was collected three times, at 18-month intervals, between 2013 and 2018. A Sankey diagram illustrated longitudinal ART trends among participants who reported their ART use. Cross-sectional analysis using 2017-2018 data included participants who self-reported their regimen, ART adherence, and viral load. Utilizing logistic regression models, self-reported adherence (percentage of ART taken in the past month) and self-reported VS (most recent <50 copies/mL) were investigated. RESULTS: Among participants reporting ART use (n = 1187), integrase inhibitor use increased from 13.6% (n = 162) to 30.6% (n = 363), while other classes decreased. Among 617 participants assessed between 2017 and 2018, <70% adherence levels (adjusted odds ratio [aOR]: 0.06, 95% confidence interval [CI]: 0.01-0.27), 70%-79% adherence (aOR: 0.29, 95% CI: 0.05-1.77) and 80%-89% (aOR: 0.21, 95% CI: 0.05-0.86) were associated with lower odds of reporting VS compared with ≥95% adherence, although statistically not significant for 70%-79% adherence. No difference was found for 90%-94% adherence (aOR: 1.04, 95% CI: 0.20-5.32) compared with ≥95%. CONCLUSION: Our findings suggest that ART adherence levels lower than 90% are associated with a lower likelihood of VS among women living with HIV.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".