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Record W2170561155 · doi:10.1586/14787210.1.1.75

Optimized antiretroviral therapy: the role of therapeutic drug monitoring and pharmacogenomics

2003· review· en· W2170561155 on OpenAlexaff
Rolf P. G. van Heeswijk

Bibliographic record

VenueExpert Review of Anti-infective Therapy · 2003
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsTherapeutic drug monitoringPharmacogenomicsTherapeutic indexPharmacologyProtease inhibitor (pharmacology)MedicineDrugTherapeutic effectProteaseReverse transcriptaseAntiretroviral therapyHuman immunodeficiency virus (HIV)ImmunologyBiologyViral loadRNA

Abstract

fetched live from OpenAlex

The identification of relationships between plasma concentrations of protease inhibitors and later, the non-nucleoside reverse transcriptase inhibitors and their efficacy and toxicity has caused a growing interest in the concept of therapeutic drug monitoring in the treatment of HIV infection. Some evidence suggests a beneficial effect of therapeutic drug monitoring of protease inhibitors. More recently, plasma protease inhibitor concentrations have been combined with viral susceptibility into one parameter, the inhibitory quotient, which may be a better predictor of the response to therapy, especially in pretreated patients. This paper reviews the current literature on therapeutic drug monitoring and the inhibitory quotient in the management of HIV infection and speculates on the future role of pharmacogenomics in this therapeutic area.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.364
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2003
Admission routes1
Has abstractyes

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