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Record W3035115074 · doi:10.1111/fcp.12578

Estimates of population‐based incidence of malignant arrhythmias associated with medication use—a narrative review

2020· review· en· W3035115074 on OpenAlexaffabout
Yichang Huang, Mhd Wasem Alsabbagh

Bibliographic record

VenueFundamental and Clinical Pharmacology · 2020
Typereview
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsIncidence (geometry)MedicinePopulationMedical prescriptionTorsades de pointesEpidemiologyIntensive care medicineLong QT syndromeCategorizationEmergency medicineInternal medicineQT intervalEnvironmental healthPharmacologyComputer science

Abstract

fetched live from OpenAlex

Certain medications are reported to be associated with acquired long-QT syndrome (ALQTS), which can degenerate into a potentially severe 'malignant' arrhythmia known as torsades de pointes (TdP). However, population-based estimations of the incidence of medication-associated malignant arrhythmia are limited. The purpose of this article is to review the clinical symptoms, cellular mechanism, categorization, and risk factors of these malignant arrhythmias, as well as illustrate results and methodological limitations of epidemiological literature which have previously estimated population-based incidence of ALQTS and malignant arrhythmia. Administrative databases in universal healthcare systems (such as Canada) can be used to provide a robust estimate of this incidence. We present a valid operational definition of medication-associated malignant arrhythmia, using Canadian hospital administrative data linked to prescription databases that can be used to estimate the population-based incidence. An estimation of incidence may have important implications with regard to understanding the potential widespread distribution of this adverse effect-which may influence medication prescribing patterns.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.837
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.062
GPT teacher head0.428
Teacher spread0.366 · 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 designOther design
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

Citations2
Published2020
Admission routes2
Has abstractyes

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