Bibliographic record
Abstract
PURPOSE OF REVIEW: This review discusses the use of the inhibitory quotient in light of therapeutic drug monitoring of antiretroviral drugs. The inhibitory quotient is a parameter that combines viral resistance data with drug exposure data, and has its main role in therapeutic drug monitoring of protease inhibitors in experienced patients. Data from recent clinical studies investigating inhibitory quotient cutoffs to be used in therapeutic drug monitoring will be reviewed. In addition points for discussion regarding the use and study of inhibitory quotients will be presented. RECENT FINDINGS: A number of studies generated data on the use of the inhibitory quotient in general and the genotypic inhibitory quotient in particular. Most of these studies define a cutoff inhibitory quotient value, above which the virological response rate is higher. These cutoff values can be used in therapeutic drug monitoring and give guidance to the clinician on dose adjustments. Genotypic inhibitory quotient cutoff values are available for amprenavir, atazanavir, darunavir, lopinavir, saquinavir and tipranavir. SUMMARY: The inhibitory quotient is becoming a valuable tool in therapeutic drug monitoring. At this moment most data are available for the genotypic inhibitory quotient. Nevertheless, a consensus needs to be reached on a number of items, including the methods to study inhibitory quotient as well as the mathematical and virological background.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".