An up-to-date evaluation of dolutegravir/abacavir/lamivudine for the treatment of HIV
Bibliographic record
Abstract
INTRODUCTION: There are more than 30 agents available for the treatment of HIV with guidelines shifting toward integrase strand transfer inhibitors (INSTIs) as part of first line therapy. The fixed dose combination of dolutegravir (DTG), abacavir (ABC), and lamivudine (3TC) is a convenient, well tolerated, and highly effective option for treating HIV infection and remains a first line therapy across several prominent guidelines. AREAS COVERED: In this drug evaluation, the authors provide a comprehensive overview of DTG/ABC/3TC for the treatment of HIV including the pharmacokinetics, pharmacodynamics, efficacy, safety, and tolerability. The authors also provide the reader with their expert perspectives on this particular treatment strategy. EXPERT OPINION: While DTG/ABC/3TC remains a valuable HIV treatment option, newer combination regimens have entered the market. Bictegravir with tenofovir alafenamide and emtricitabine offers the benefit of same day initiation and efficacy in hepatitis B co-infection, while new two-drug regimens enhance the simplicity of HIV treatment. Continued study is required into the mechanisms and optimal management strategies for weight gain for many regimens, including DTG/ABC/3TC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".