Bibliographic record
Abstract
Efavirenz is in the non‐nucleoside reverse transcriptase inhibitor category of HIV antiretroviral medicines. It is an in vivo inducer of the CYP3A4 isoenzyme within the cytochrome P450 (CYP450) system, and an in vitro inhibitor of the system’s CYP2C9/2C19, 3A4 and 2B6 isoenzymes; as a result, concentrations of psychotropic drugs can be increased or decreased depending on the specific enzyme pathway involved in their metabolism. CYP3A4 is responsible for metabolizing many benzodiazepines and other psychotropics, as well as selective serotonin reuptake inhibitors and tricyclic antidepressants. As an inducer of CYP3A4, efavirenz can increase the rate at which these agents are metabolized, resulting in administered psychotropic drug levels that are below their therapeutic thresholds. Conversely, efavirenz is an inhibitor of CYP2B6, which metabolizes agents such as bupropion; consequently, bupropion levels in the blood can increase. Given the existing conflicting data, the clinician may find it impractical to use an evidence‐based approach when concomitantly prescribing efavirenz and psychotropic drugs to their HIV patients. Instead, it may be preferable to use a more pragmatic approach that applies knowledge of the most current pharmacological and pharmacokinetic data for psychotropics and non‐nucleoside reverse transcriptase inhibitors, which may help better predict their potential interactions.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".