Bibliographic record
Abstract
Efavirenz (Sustiva), Bristol-Myers Squibb) is a non-nucleoside reverse transcriptase inhibitor that has been used successfully since the late 1990s to treat HIV-1 infection, and has since become a cornerstone of antiretroviral therapy. The efficacy and potency of efavirenz has been established in many clinical trials and cohort studies, where it has been compared with unboosted or ritonavir (Norvir, Abbott Laboratories Ltd)-boosted protease inhibitors, nevirapine (Viramune, Boehringer Ingelheim Ltd); and three nucleoside analog-based regimens. Pharmacokinetics allowing for a convenient once-daily administration make efavirenz one of the first agents to be included in once-daily regimens. Tolerability of efavirenz is satisfactory, although CNS-related toxicity can occur, and is still poorly understood. New insights into the pharmacokinetics of efavirenz could help to manage this unwanted toxicity. This drug profile will examine the principal data concerning the efficacy, pharmacokinetics and safety that have made efavirenz a standard of care in HIV-1 therapy, and will comment on new data that could change the way efavirenz is used in the near future.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.002 |
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".