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Record W2806558185 · doi:10.14740/cr724w

Revisiting the Role of Antiarrhythmic Drugs in Prevention of Atrial Fibrillation Recurrence: A Single Center Retrospective Review

2018· article· en· W2806558185 on OpenAlexvenueno aff
Daniel AN Mascarenhas, Munish Sharma

Bibliographic record

VenueCardiology Research · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAmiodaroneRetrospective cohort studySinus rhythmAtrial fibrillationInternal medicineCardiologySotalolSingle Center

Abstract

fetched live from OpenAlex

BACKGROUND: We conducted a retrospective analysis to revisit the efficacy of four different commonly used antiarrhythmic drugs (AADs) in a single community hospital setting in the U.S. We used cardiac implantable electronic devices (CIEDs) to continuously monitor the patients for maintenance of sinus rhythm. The CIEDs in our study included insertable cardiac monitor (ICM), permanent pacemaker (PPM) and cardiac resynchronization therapy-defibrillator (CRT-D). The aim was to compare efficacy of commonly used AADs for maintenance of sinus rhythm in atrial fibrillation (AF) patients. METHODS: We conducted our retrospective study in a real world practice setting. We analyzed electronic medical records of 145 consecutive patients with paroxysmal and persistent AF who were treated with AADs for maintenance of sinus rhythm between the period of April 2014 and February 2018. RESULTS: Total 34 out of 145 patients (23.45%) had AF recurrence. The mean duration of first AF recurrence in total patient cohort was 18.01 ± 12 months. There was no major difference in efficacy in terms of prevention of first episode of AF recurrence among commonly used class III and class IC AADs. CONCLUSIONS: Higher doses clearly seem to be more effective in preventing the recurrence of AF in class III AADs; sotalol and amiodarone. Use of CIEDs helps to continuously monitor patients for recurrence of AF and detects proarrhythmic effects of AADs.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.033
GPT teacher head0.371
Teacher spread0.338 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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