Real-world persistence of bictegravir versus dolutegravir single-tablet regimens: A retrospective cohort study in a large urban Canadian HIV clinic
Bibliographic record
Abstract
Background Single-tablet regimens (STRs) with integrase inhibitors, bictegravir (BIC) or dolutegravir (DTG), are favored in HIV treatment for their efficacy and convenience. This study compares persistence—time from initiation to discontinuation—between BIC/emtricitabine (FTC)/tenofovir alafenamide (TAF) and DTG-containing STRs at a Toronto HIV clinic . Methods A retrospective cohort analysis was conducted on 1732 adults with HIV at Maple Leaf Medical Clinic who initiated or switched to BIC/FTC/TAF or DTG-containing STRs from 2016 to 2022. Persistence was measured in days until discontinuation. Kaplan-Meier curves and Cox models evaluated time-to-discontinuation and associated risks. Reasons for discontinuation were categorized into adverse events, patient preference, cost, compliance, physician preference, virologic failure, and others. Results Among 1732 participants (median age 48 years, 88.7% cisgender men), 387 (22.3%) discontinued their STRs after a median of 402 days. BIC/FTC/TAF had a lower discontinuation rate (18.9%) compared to DTG-containing STRs (29.9%) (HR = 0.74, 95% CI: 0.60–0.92). Adverse events were the primary reason for discontinuation, with BIC having lower rates (9.6% vs. 12.5% for DTG). Discussion BIC/FTC/TAF demonstrated higher persistence and fewer adverse events than DTG-containing STRs, aiding personalized HIV treatment decisions for better long-term outcomes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".