Does switching from multiple to single-tablet regimen containing the same antiretroviral drugs improve adherence? A group-based trajectory modeling analysis
Bibliographic record
Abstract
Combination Antiretroviral Therapy (cART) in single-tablet regimens (STR) is a simplification strategy that can potentially improve medication adherence and clinical outcomes. We conducted a retrospective cohort study of 1206 patients using efavirenz, tenofovir and lamivudine in multiple-tablet regimen who switched to the STR containing the same active ingredients in a southeast metropolis in Brazil. We measured adherence using the proportion of days covered (PDC≥95%) and evaluated this outcome before and after the switch using paired non-parametric statistics. Additionally, we used group-based trajectory modeling to identify adherence patterns to cART for each period and evaluate the migration behavior of patients between the trajectory groups. We observed a 14% increase in the proportion of adherent patients after switching to STR and a 6.2% increase in the proportion of patients with CD4 count>500 cells/μl (p < 0.001), without changes in viral load outcomes. We identified four adherence trajectories in each period. Most patients (60%, n = 722) migrated towards a group with better adherence trajectory or remained in the trajectory group with the highest probability of adherence after the switch. Our findings suggest that the implementation of the STR had a positive impact on adherence and CD4 count. This may potentially improve virologic outcomes later on treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".