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Record W2133879403 · doi:10.1016/j.eupc.2005.03.016

Effects of antiarrhythmic drug therapy on atrioventricular nodal function during atrial fibrillation in humans*

2005· article· en· W2133879403 on OpenAlexafffund
Laurence Mangin, Alain Vinet, Pierre Pagé, Leon Glass

Bibliographic record

VenueEP Europace · 2005
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcGill UniversityUniversité de MontréalHôpital du Sacré-Cœur de Montréal
FundersNational Institutes of HealthMitacsCanadian Institutes of Health ResearchFondation Simone et Cino Del Duca
KeywordsMedicineAtrial fibrillationAmiodaroneMetoprololAtrioventricular nodeCardiologyInternal medicineAtrium (architecture)AnesthesiaTachycardia

Abstract

fetched live from OpenAlex

AIMS: To assess the effects of metoprolol and amiodarone on atrial and ventricular activity during atrial fibrillation (AF) in post-surgical patients, and to develop and use a mathematical model of the atrioventricular (AV) node during AF that incorporates parameters describing the properties of the AV node to evaluate the physiological basis of the drug effects. METHODS AND RESULTS: Ten post-surgical patients were evaluated where three received no medical therapy, three received metoprolol, three received amiodarone, and one received both metoprolol and amiodarone. The medications led to increases of 37-310 ms in the mean VV interval in treated patients, but much smaller changes in the mean AA intervals in the right and left atria. The mathematical model incorporating a random influence of the concealed conduction parameter was capable of reproducing the histograms of the VV intervals based on the input from the right atrium by systematically searching parameter space. CONCLUSIONS: Changes in the ventricular rate are mainly due to the alteration in the AV nodal properties rather than changes in the atrial rhythm. The medications can display differential effects on the physiological properties of the AV node, and therefore the mathematical model may help to identify novel pharmacological targets.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.078
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.272
Teacher spread0.258 · 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 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

Citations35
Published2005
Admission routes2
Has abstractyes

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