Dual Therapy Treatment Strategies for the Management of Patients Infected with HIV: A Systematic Review of Current Evidence in ARV-Naive or ARV-Experienced, Virologically Suppressed Patients
Bibliographic record
Abstract
We reviewed the current literature regarding antiretroviral (ARV)-sparing therapy strategies to determine whether these novel regimens can be considered appropriate alternatives to standard regimens for the initial treatment of ARV-naive patients or as switch therapy for those patients with virologically suppressed HIV infection.A search for studies related to HIV dual therapy published from January 2000 through April 2014 was performed using Biosis, Derwent Drug File, Embase, International Pharmaceutical Abstracts, Medline, Pascal, SciSearch, and TOXNET databases; seven major trial registries, and the abstracts of major conferences. Using predetermined criteria for inclusion, an expert review committee critically reviewed and qualitatively evaluated all identified trials for efficacy and safety results and potential limitations.Sixteen studies of dual therapy regimens were critiqued for the ARV-naive population. Studies of a protease inhibitor/ritonavir in combination with the integrase inhibitor raltegravir or the nucleoside reverse transcriptase inhibitor lamivudine provided the most definitive evidence supporting a role for dual therapy. In particular, lopinavir/ritonavir or darunavir/ritonavir combined with raltegravir and lopinavir/ritonavir combined with lamivudine demonstrated noninferiority to standard of care triple therapy after 48 weeks of treatment. Thirteen trials were critiqued in ARV-experienced, virologically suppressed patients. The virologic efficacy outcomes were mixed. Although overall data regarding toxicity are limited, when compared with standard triple therapy, certain dual therapy regimens may offer advantages in renal function, bone mineral density, and limb fat changes; however, some dual combinations may elevate lipid or bilirubin levels.The potential benefits of dual therapy regimens include reduced toxicity, improved tolerability and adherence, and reduced cost. Although the data reviewed here provide valuable insights into the effectiveness and tolerability of dual therapy regimens, it remains unclear whether these potential benefits can be maintained long-term. Appropriately powered studies with longer follow-up periods are needed to more definitively assess potential toxicity reduction advantages with dual therapy.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".