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Record W2898149304 · doi:10.3851/imp3274

Optimizing Concentrations of Concomitant Antiretrovirals by Reducing Etravirine Doses: Two Case Reports of Complex Drug-drug Interactions

2018· article· en· W2898149304 on OpenAlexaff
Jean-Simon Denault, Jean-François Cabot, Hugo Langlois, Suzanne Marcotte, Nancy L. Sheehan

Bibliographic record

VenueAntiviral Therapy · 2018
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsHôpital du Sacré-Cœur de MontréalMcGill University Health CentreUniversité de MontréalCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsEtravirineDarunavirElvitegravirDolutegravirRaltegravirDrugPharmacologyMedicineConcomitantDrug interactionEnfuvirtideMaravirocHuman immunodeficiency virus (HIV)Internal medicineAntiretroviral therapyVirologyViral loadImmunology

Abstract

fetched live from OpenAlex

We report the cases of two treatment-experienced HIV-infected patients with complex antiretroviral regimens that showed significant drug-drug interactions with etravirine. Unexpectedly high etravirine concentrations likely caused subtherapeutic levels of darunavir, elvitegravir and dolutegravir through concentration-dependent metabolic induction. Therapeutic drug monitoring allowed safe etravirine dose decreases to manage these 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 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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.034
GPT teacher head0.337
Teacher spread0.303 · 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 designCase report
Domainnot available
GenreEmpirical

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

Citations1
Published2018
Admission routes1
Has abstractyes

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