Sequencing of Therapy to Avoid Resistance and the Need for New Antiretroviral Drugs in the Treatment of HIV Disease
Bibliographic record
Abstract
HIV-1 drug regimens now offer more potent, less toxic and more durable choices. However, strategies addressing convenient sequential use of active antiretroviral combinations are rarely presented in the literature. Studies have seldom directly addressed this issue, despite it being a matter of daily use in clinical practice. This is, in part, because of the complexity of HIV-1 resistance information. Nevertheless, several principles can effectively assist the planning of antiretroviral drug sequencing. The introduction of tenofovir, abacavir and emtricitabine into current nucleoside backbone options, with each of them selecting for an individual pattern of resistance mutations, now permits sequencing in the context of previously popular thymidine analogs (zidovudine and stavudine). Similarly, newer ritonavir-boosted protease inhibitors could be potentially sequenced in a manner that uses the least cross-resistance prone PI at the start of therapy while leaving the most cross-resistance prone drugs for later, as long as there is rationale to employ such a compound because of its utility against commonly observed drug-resistant forms of the virus. The ability to sequence new therapies will be enhanced by availability of new antiretroviral drugs.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".