Strategies for the Optimal Sequencing of Antiretroviral Drugs Toward Overcoming and Preventing Drug Resistance
Bibliographic record
Abstract
Drug regimens now offer more potent, less toxic and more durable choices in the treatment of HIV disease than ever before. This has led to a need to consider the convenient, sequential use of active antiretroviral combinations. Ritonavir-boosted protease inhibitors (PIs) can now 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 drug for later, if the latter retains activity against commonly observed drug-resistant forms. Similarly, such new drugs as tenofovir, abacavir and emtricitabine, which make up current nucleoside backbone options, can be potentially sequenced, since each of them selects for an individual pattern of resistance mutations that are generally distinct from those selected by previously popular thymidine analogs such as zidovudine and stavudine.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".