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Record W2915205180 · doi:10.14740/cr806

A Retrograde Transaortic/Transmitral Approach for Radiofrequency Ablation of Adenosine-Sensitive Atrial Tachycardia Arising Near the Apex of Koch’s Triangle: A Case Study Approach and Review of the Literature

2019· article· en· W2915205180 on OpenAlexvenueno aff
Ján Hluchý, M. van Bracht, Bodo Brandts

Bibliographic record

VenueCardiology Research · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCardiologyInternal medicineRadiofrequency ablationAblationApex (geometry)Atrial tachycardiaAdenosineTachycardiaCatheter ablationAnatomy

Abstract

fetched live from OpenAlex

Atrial tachycardias (ATs) are relatively uncommon and their mechanisms include reentry or may be focal. The characteristics and radiofrequency (RF) ablation of adenosine-sensitive AT arising near the apex of Koch's triangle have been reported. We report a case of successful RF ablation of this tachycardia by using a retrograde transaortic/transmitral approach. An electrophysiological study performed during sinus rhythm showed continuous anterograde atrioventricular nodal and no ventriculoatrial conduction. The tachycardia could be induced and terminated by atrial extrastimulation and rapid atrial pacing. Although inverse resetting response pattern was observed, tachycardia termination by intravenous low-dose adenosine was rather suggestive of triggered activity as the mechanism. The earliest atrial activation was recorded in the His-bundle region. After ineffective right-sided attempts and failure to find earlier atrial activity during mapping the noncoronary sinus of Valsalva, this tachycardia was successfully ablated from the left atrial septum by using a retrograde transaortic/transmitral approach. This report demonstrates the feasibility of a novel retrograde left atrial approach for RF ablation of adenosine-sensitive AT originating from the apex of Koch's triangle.

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)0.000
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 score0.165
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.047
GPT teacher head0.359
Teacher spread0.313 · 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
Published2019
Admission routes1
Has abstractyes

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