Can Structured Treatment Interruptions (STIs) Be Used as a Strategy to Decrease Total Drug Requirements and Toxicity in HIV Infection?
Bibliographic record
Abstract
Structured treatment interruptions (STIs) are a new strategy under investigation in clinical trials involving a number of different HIV-infected populations. These populations include patients with prolonged HIV RNA suppression who were treated in either seroconversion or later in disease, and patients with virologic failure despite HAART, prior to the initiation of a salvage regimen. The goals of STI vary in each of these groups. Until the results of clinical trials are available, the use of STIs must be considered experimental. There are a number of potential risks, including the loss of a significant number of CD4 cells with the development of opportunistic infections, rebound of HIV RNA, emergence of drug resistance, and reseeding of viral reservoirs. However, STIs also hold the promise for decreasing antiretroviral drug burden and toxicity, and improving quality of life. Given that much of the world's population infected with HIV does not have access to continuous HAART, the development of strategies that could decrease overall drug burden and cost is important. This paper provides an update of the recently published and presented studies on the use of STIs in various populations of HIV-infected patients. In particular, it discusses what is known and unknown about the relative risks and benefits of this approach, and what studies are ongoing. Lastly, it identifies how the use of STIs could decrease drug burden and toxicity in patients receiving 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 | 0.004 | 0.013 |
| 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".