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Record W4381848313 · doi:10.1002/hsr2.1375

Adverse events with quetiapine and clarithromycin coprescription: A population‐based retrospective cohort study

2023· article· en· W4381848313 on OpenAlexafffund
Kevin Yau, Eric McArthur, Nivethika Jeyakumar, Flory T. Muanda, Richard B. Kim, Kristin K. Clemens, Ron Wald, Amit X. Garg

Bibliographic record

VenueHealth Science Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsSt. Michael's HospitalWestern UniversityInstitute for Clinical Evaluative SciencesLondon Health Sciences CentreLawson Health Research InstituteThinkpath Engineering Services (Canada)
FundersBanting and Best Diabetes Centre, University of TorontoLawson Health Research InstituteCanadian Institutes of Health ResearchKidney Foundation of CanadaUniversity of TorontoDepartment of Medicine, University of TorontoSchulich School of Medicine and DentistryAcademic Medical Organization of Southwestern Ontario
KeywordsQuetiapineClarithromycinRetrospective cohort studyMedicineCohortAdverse effectPopulationCohort studyPediatricsPsychiatryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Background and Aims: Quetiapine is an atypical antipsychotic predominantly metabolized by the cytochrome P450 3A4 (CYP3A4) enzyme. We studied the risk of adverse events following coprescription of clarithromycin (a strong CYP3A4 inhibitor) versus azithromycin (not a CYP3A4 inhibitor) in quetiapine users. Materials and Methods: = 25,267). The primary outcome was the composite of hospital encounters with encephalopathy (defined as a diagnosis of delirium, disorientation, transient alteration of awareness, transient ischemic attack, or unspecified dementia), a fall, or a fracture within 30 days of new coprescription. Secondary outcomes were individual components of the composite outcome, hospital encounter with computed tomography (CT) head scan, and all-cause mortality. Results: Coprescription of clarithromycin versus azithromycin with quetiapine was associated with a higher risk of the primary composite outcome (365 of 16,909 clarithromycin users [2.2%] vs. 309 of 16,929 azithromycin users [1.8%]; absolute risk increase, 0.34% [95% confidence interval, CI, 0.04-0.63]; relative risk [RR], 1.19 [95% CI, 1.02-1.38]). This was primarily driven by an increase in fragility fractures (78 of 16,909 clarithromycin users [0.5%] vs. 45 of 16,923 azithromycin users [0.3%]; absolute risk increase, 0.20% [95% CI, 0.07-0.32]; RR, 1.74 [95% CI, 1.21-2.52]). Hospital encounters with a CT head scan were higher in clarithromycin users (220 of 16,909 [1.3%] vs. 175 of 16,923 azithromycin users [1.0%]; absolute risk increase, 0.27% [95% CI, 0.04-0.50]; RR, 1.26 [95% CI, 1.04-1.54]), but there was no difference in hospital encounters with encephalopathy, falls, or all-cause mortality between macrolide groups. Conclusion: Among adults taking quetiapine, concurrent use of clarithromycin compared with azithromycin was associated with a small but statistically greater 30-day risk of a hospital encounter for encephalopathy, falls, or fracture, which was predominantly related to a higher rate of fragility fractures.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.365
Teacher spread0.334 · 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 designObservational
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

Citations8
Published2023
Admission routes2
Has abstractyes

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