Virological Outcomes After Switching to Abacavir/Lamivudine/Dolutegravir Combined with Adherence Support in People Living with HIV with Poor Adherence: A Phase IV, Multicentre Randomized Prospective Open Label Study (TriiADD-CTN 286)
Bibliographic record
Abstract
Background: Many people living with HIV struggle to consistently adhere to antiretroviral therapy, fail to achieve long-term virologic control and remain at risk for HIV-related disease progression, development of resistance and may transmit HIV infection to others. Objective: To determine if switching from current multi-tablet (curART) to single-tablet antiretroviral therapy (abacavir/lamivudine/dolutegravir; ABC/3TC/DTG), both combined with individualized adherence support, would improve HIV suppression in non-adherent vulnerable populations. Methods: TriiADD was an investigator-initiated randomized, multicentre, open label study. HIV+ adults with documented non-adherence on curART were randomized in a 1:1 ratio to immediately switch to ABC/3TC/DTG or to continue curART. Both arms received adherence support. The primary outcome was the proportion of participants in each arm with HIV RNA < 50 copies/mL 24 weeks after randomization. Results: In total, 50 people were screened and 27 randomized from 11 sites across Canada before the trial was stopped early due to slow recruitment. Participants were predominantly from ethnocultural communities, Indigenous people and/or had a history of injection drug use. The proportion achieving HIV RNA < 50 copies/mL at week 24 was 4/12 (33%) in the curART arm vs 7/13 (54%) in the ABC/3TC/DTG arm; median Bayesian risk difference, 5% (95% CrI, -17 to 28%) higher for those randomized to ABC/3TC/DTG. We encountered difficulties with recruitment of participants without prior drug resistance, retention despite intensive support, reliably measuring adherence and in overcoming entrenched adherence barriers. Conclusion: Results of our trial are consistent with a slight improvement in viral suppression in a vulnerable population when a single tablet regimen is combined with patient-level adherence support. Beyond treatment simplicity and tolerability, tailored interventions addressing stigma and social determinants of health are still needed. The numerous challenges we encountered illustrate how randomised trials may not be the best approach for assessing adherence interventions in vulnerable populations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".