Backbones versus core agents in initial ART regimens: one game, two players
Bibliographic record
Abstract
The advances seen in ART during the last 30 years have been outstanding. Treatment has evolved from the initial use of single agents as monotherapy. The ability to use HIV RNA as a surrogate marker for clinical outcomes allowed the more rapid evaluation of new therapies. This led to the understanding that triple-drug regimens, including a core agent (an NNRTI or a boosted PI) and two NRTIs, are optimal. These combinations have demonstrated continued improvements in their efficacy and toxicity as initial therapy. However, the need for pharmacokinetic boosting, with potential drug-drug interactions, or residual issues of efficacy or toxicity have persisted for some agents. Most recently, integrase strand transfer inhibitors, particularly dolutegravir, have shown unparalleled safety and efficacy and are currently the core agents of choice. Regimens that included only core agents or only backbone agents have not been as successful as combined therapy in antiretroviral-naive patients. It appears that at least one NRTI is needed for optimal performance and lamivudine and emtricitabine may be the ideal candidates. Several studies are ongoing of agents with longer dosing intervals, lower cost and new NRTI-saving strategies to address unmet needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".