Comparative Clinical Outcomes With Scale-up of Dolutegravir as First-Line Antiretroviral Therapy in Ukraine
Bibliographic record
Abstract
BACKGROUND: Achievement of the UNAIDS 95-95-95 targets requires ARV regimens that are easy to use, well-tolerated, and cost-effective. Dolutegravir (DTG)-based regimens are efficacious and less costly than other common first-line regimens. This study assessed real-world effectiveness of DTG regimens in treatment-naive people living with HIV in Ukraine. METHODS: We extracted data from the national Medical Information System on all adult patients who initiated antiretroviral therapy (ART) with DTG, lopinavir/ritonavir, or efavirenz (EFV) between October 2017 and June 2018, at 23 large clinics in 12 regions of Ukraine. Viral suppression at 12 ± 3 months and retention at 12 months after treatment initiation were the outcomes of interest. RESULTS: Of total 1057 patients, 721 had a viral load test within the window of interest, and 652 (90%) had viral load of ≤ 200 copies/mL. The proportion with suppression was lower in the EFV group [aOR = 0.4 (95% confidence interval: 0.2 to 0.8)] and not different in the LPV group [aOR = 1.6 (0.5 to 4.9)] compared with the DTG group. A 24-month or longer gap between diagnosis and treatment was associated with lower odds of suppression [aOR = 0.4 (0.2 to 0.8)]. Treatment retention was 90% (957/1057), with no significant difference by regimen group. History of injecting drug use was associated with decreased retention [aOR = 0.5 (0.3 to 0.8)]. CONCLUSIONS: DTG-based regimens were comparable with LPV and more effective than EFV in achieving viral suppression among ART-naive patients in a multisite cohort in Ukraine. Treatment retention was equally high in all 3 groups. This evidence from Ukraine supports the ART Optimization Initiative as a strategy to improve efficiency of the ART program without negatively affecting patient clinical 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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".