Two‐drug <i>vs</i>. three‐drug combinations for <scp>HIV</scp>‐1: Do we have enough data to make the switch?
Bibliographic record
Abstract
Three-drug combination antiretroviral therapy (ART) became available in 1996, dramatically improving the prognosis of people living with HIV. The clinical benefits of ART are due to the sustained viral load suppression and CD4 T cell gains. Major drawbacks of the first ART regimens were adverse events, and high pill burden, which led to the reduction of drug adherence resulting in frequent treatment discontinuations and the development of drug resistance. Due to increased viral potency of new antiretroviral drugs consideration of a two-drug combination therapy repositioning occurred in an effort to reduce adverse events, drug-drug interactions and cost, while maintaining a sustained antiviral effect. Various combinations of two-drug regimens have been studied, and non-inferiority compared to a three-drug regimen has been shown only for some of them. In addition, a two-drug combination regimen may not be suitable for every patient, especially those who are pregnant, those with tuberculosis or coexisting HBV infection. Furthermore no information has been generated concerning the secondary transmission of HIV from patients who have undetectable plasma viral load on two-drug regimens. Additional studies of two-drug combinations are also necessary to evaluate the debated existence of low viral replication in tissues and on immune activation. While there is no urgent need to routinely switch patients to two-drug combination therapy, due to the availability of drug combinations without significant toxicities, dual regimens represent a suitable option that deserve long-term evaluation before being introduced to clinical practice.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".