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Record W2090067514 · doi:10.1097/fjc.0b013e31821b8608

Atrial Selective Effects of Intravenously Administered Vernakalant in Conscious Beagle Dogs

2011· article· en· W2090067514 on OpenAlexaff
Jeff Bechard, Marc Pourrier

Bibliographic record

VenueJournal of Cardiovascular Pharmacology · 2011
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsCardiome (Canada)
Fundersnot available
KeywordsMedicinePR intervalCardiologyQRS complexBeagleInternal medicineAtrial fibrillationQT intervalSinus rhythmRefractory periodSotalolHeart rateAnesthesiaAnti-Arrhythmia AgentsBlood pressureAntiarrhythmic agentHeart disease

Abstract

fetched live from OpenAlex

Vernakalant is a relatively atrial-selective antiarrhythmic drug approved for the conversion of recent onset atrial fibrillation in Europe and is under regulatory review in the United States. In this study, we examined the effects of intravenously administered vernakalant (5, 10, and 20 mg/kg) on blood pressure, heart rate, and the electrocardiogram in conscious male beagle dogs and compared them with those of orally administered dl-sotalol (32 mg/kg). Vernakalant had no consistent dose-dependent effects on the heart rate or mean arterial pressure. Although vernakalant inhibits I(Kr), it tended to decrease the QTc interval but only at the top dose and later time points. The most striking effect of vernakalant on the electrocardiogram was a dose-dependent and selective slowing of atrial conduction (P-wave duration), with no effect on ventricular conduction (QRS duration). In contrast, treatment with dl-sotalol resulted in a marked and statistically significant prolongation of PR and QTc intervals with no effect on QRS or P-wave duration, consistent with its known class II and III antiarrhythmic actions. These results provide further evidence that vernakalant is unlikely to alter ventricular refractoriness or conduction at plasma concentrations in excess of those necessary for conversion of atrial fibrillation to sinus rhythm in 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
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 head0.012
GPT teacher head0.259
Teacher spread0.247 · 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 designBench or experimental
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
Published2011
Admission routes1
Has abstractyes

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