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Record W4394869921 · doi:10.1093/jac/dkae103

Triazole antifungal drug interactions—practical considerations for excellent prescribing

2024· review· en· W4394869921 on OpenAlexaff
Russell E. Lewis, Saarah Niazi-Ali, Andrew McIvor, Souha S. Kanj, Johan Maertens, Matteo Bassetti, Deborah J. Levine, Andreas H. Groll, David W. Denning

Bibliographic record

VenueJournal of Antimicrobial Chemotherapy · 2024
Typereview
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsMcMaster University
FundersRoyal College of Pathologists of Australasia
KeywordsMedicineItraconazoleFluconazolePharmacologyPosaconazoleDrugAntifungal drugAntifungalDermatology

Abstract

fetched live from OpenAlex

Systemic antifungal therapy is critical for reducing the mortality from many invasive and chronic fungal infections. Triazole antifungals are the most frequently prescribed antifungals but require attention to dosing and drug interactions. Nearly 600 severe drug-drug interactions and over 1100 moderate interactions requiring dose modifications are described or anticipated with systemic antifungal agents (see https://www.aspergillus.org.uk/antifungal-drug-interactions/). In this article, we address the common and less common, but serious, drug interactions observed in clinical practice with triazole antifungals, including a group of drugs that cannot be prescribed with all or most triazole antifungals (ivabradine, ranolazine, eplerenone, fentanyl, apomorphine, quetiapine, bedaquiline, rifampicin, rifabutin, sirolimus, phenytoin and carbamazepine). We highlight interactions with drugs used in children and new agents introduced for the treatment of haematological malignancies or graft versus host disease (midostaurin, ibrutinib, ruxolitinib and venetoclax). We also summarize the multiple interactions between oral and inhaled corticosteroids and triazole antifungals, and the strategies needed to optimize the therapeutic benefits of triazole antifungal therapy while minimizing potential harm to patients.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.092
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
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 head0.111
GPT teacher head0.420
Teacher spread0.308 · 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 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

Citations33
Published2024
Admission routes1
Has abstractyes

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