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Record W2099286443 · doi:10.1186/1752-1947-3-9307

Correlations between atazanavir Ctrough and hyperbilirubinemia: a case report

2009· article· en· W2099286443 on OpenAlexaff
Alessia Uglietti, Stefano Novati, Roberto Gulminetti, Renato Maserati

Bibliographic record

VenueJournal of Medical Case Reports · 2009
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsAtazanavirRitonavirMedicineTolerabilityCobicistatUnconjugated hyperbilirubinemiaDiscontinuationTherapeutic drug monitoringPharmacologyInternal medicineDrugAntiretroviral therapyBilirubinHuman immunodeficiency virus (HIV)Viral loadVirologyAdverse effect

Abstract

fetched live from OpenAlex

INTRODUCTION: Hyperbilirubinemia is a common side effect of the antiretroviral agent atazanavir but is generally reversible upon discontinuation of treatment. We used therapeutic drug monitoring to investigate the occurrence of hyperbilirubinemia in a 49-year-old Hispanic man infected with HIV, following an overdose of ritonavir in ritonavir-boosted atazanavir therapy. CASE PRESENTATION: A 49-year-old Hispanic man with HIV who had received several highly active antiretroviral therapy regimens over a number of years including atazanavir-containing regimens, was diagnosed with hyperbilirubinemia. An inappropriate doubling of ritonavir boosting resulted in a high atazanavir C(trough )and an initial rise in bilirubin plasma levels. Bilirubin levels later decreased, probably as a consequence of enzyme induction, while atazanavir plasma concentrations remained elevated. CONCLUSION: This article describes an occurrence of hyperbilirubinemia in a man infected with HIV and supports the importance of therapeutic drug monitoring in investigations of hyperbilirubinemia among patients receiving antiretroviral agents. That the patient tolerated exceptionally high atazanavir levels further strengthens the tolerability profile of this drug.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.044
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.324
Teacher spread0.302 · 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.

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

Citations9
Published2009
Admission routes1
Has abstractyes

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