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Record W1979277621 · doi:10.3109/07853890.2012.732704

Update on rilpivirine: A new potent non-nucleoside reverse transcriptase inhibitor (NNRTI) of HIV replication

2012· review· en· W1979277621 on OpenAlexaff
Gerasimos J. Zaharatos, Mark A. Wainberg

Bibliographic record

VenueAnnals of Medicine · 2012
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsRilpivirineReverse transcriptaseReverse-transcriptase inhibitorVirologyMedicineNucleoside Reverse Transcriptase InhibitorDosingViral loadDrugPharmacologyAntiretroviral drugDrug resistanceLentivirusClinical trialHuman immunodeficiency virus (HIV)Antiretroviral therapyBiologyViral diseaseInternal medicineRNA

Abstract

fetched live from OpenAlex

INTRODUCTION: A combination of antiretroviral drugs (ARVs) is necessary to achieve sustained virologic suppression of HIV viral load (< 50 copies/mL). Rilpivirine (RPV) is a potent new non-nucleoside reverse transcriptase inhibitor (NNRTI) that has the potential to be part of effective ARV combinations. Here, we review currently available data on RPV from the standpoint of virologic suppression and efficacy, drug-drug interactions safety, and resistance. AREAS COVERED: This review presents data on the results of clinical trials involving RPV. The topics considered include antiviral potency, dosing, clinical utility, drug resistance, toxicity profile, and pharmacokinetics. EXPERT OPINION: RPV is a potent new addition to the antiretroviral family of drugs for use in combination therapy in previously untreated HIV-infected patients. However, caution needs to be exercised in administration of RPV to patients who initiated therapy with viral loads > 100,000 viral RNA copies/mL.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.003

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 head0.139
GPT teacher head0.390
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations12
Published2012
Admission routes1
Has abstractyes

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