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Record W4407114206 · doi:10.1212/wnl.0000000000210302

Cardiac Conduction Delay for Sodium Channel Antagonist Antiseizure Medications

2025· article· en· W4407114206 on OpenAlexafffundabout
Nathan A. Shlobin, Jimmy Li, Josemir W. Sander, Mark R. Keezer, Roland D. Thijs

Bibliographic record

VenueNeurology · 2025
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité de MontréalCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
FundersUniversité de MontréalEpilepsy SocietyEisaiTD BankSavoy FoundationGovernment of CanadaRéseau en Bio-Imagerie du QuebecCanadian Institutes of Health ResearchAngelini PharmaNational Institute for Health and Care Research
KeywordsAntagonistSodium channelMedicineSodium channel blockerCardiologyAnesthesiaInternal medicineSodiumChemistry

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: People with epilepsy are at risk of cardiac arrhythmias. Whether this association results from epilepsy, antiseizure medications (ASMs) such as sodium channel blockers (NABs), or other factors has not been systematically assessed. The aims of this study were to quantify the odds of cardiac conduction delays (CCDs) on electrocardiogram in older people with active epilepsy using vs not using NABs, to determine the prevalence of CCDs by NABs, and to examine the association of demographic and clinical factors with CCDs. METHODS: This was a cross-sectional study of the Canadian Longitudinal Study on Aging. We defined active epilepsy as self-reported epilepsy and taking ASM. Sodium channel blockers (NABs) were phenytoin, lamotrigine, carbamazepine, oxcarbazepine, or lacosamide. We compared CCDs between people with epilepsy using NABs and those not using NABs; determined the prevalence of CCDs by NAB type; and fitted a logistic regression model for each abnormal ECG outcome as a function of active epilepsy and NAB use while adjusting for demographics and clinical factors. Multiple imputations handled missing data (200 iterations). RESULTS: In total, 30,077 people, with mean age 63.0 (10.25) years and 50.9% female, were studied, including 141 people with active epilepsy who used NABs, 68 who did not use NABs, and 29,868 who did not have active epilepsy. Demographics between groups and relative to people without epilepsy were similar. People with active epilepsy taking NABs were more likely to have prolonged QRS (odds ratio [OR] = 2.85 [95% CI 1.09-7.43]) and any CCD (1.94 [1.03-3.63]) compared with those with active epilepsy without NAB. After adjusting for Framingham score and heart rate-lowering medications, NAB use was associated with prolonged QTc (OR = 1.52 [95% CI 1.06-2.18]) and any CCD (1.78 [1.16, 2.74]). The prevalence of any CCD was 36.1% [95% CI 24.2%-49.4%] for carbamazepine, 45.5% [31.7%-58.5%] for phenytoin, and 54.7% [28.9%-75.6%] lamotrigine. Epilepsy was not associated with any CCD. DISCUSSION: People with active epilepsy using NABs more commonly have CCDs. NAB use is associated with CCD, whereas active epilepsy is not.

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 score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.333
Teacher spread0.310 · 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

Citations7
Published2025
Admission routes3
Has abstractyes

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