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Record W2228826990 · doi:10.1093/europace/euv336

Characterizing fast pathway in typical and atypical atrioventricular nodal re-entrant tachycardia by atrial-His and His-atrial: more to consider

2015· letter· en· W2228826990 on OpenAlexaff
Huan Sun, Robert Lakin, Yuquan He, Ping Yang

Bibliographic record

VenueEP Europace · 2015
Typeletter
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineNODALTachycardiaCardiologyInternal medicineAtrial tachycardiaAtrioventricular nodeAtrium (architecture)PR intervalElectrophysiologyHeart rateCatheter ablationAblationAtrial fibrillationBlood pressure

Abstract

fetched live from OpenAlex

We read with interest the paper by Katritsis et al.1 published in Europace recently. In this study, the authors sought to assess the prevalence, electrophysiological characteristics, and mechanism underlying atypical atrioventricular nodal re-entrant tachycardia (AVNRT), which is not clinically well-defined. It was shown that the atrial-His (AH) interval in atypical ‘fast-slow’ (F-S) AVNRT is longer than His-atrial (HA) interval in typical ‘slow-fast’ (S-F) AVNRT, implying that different fast conduction pathways are utilized in these two types of tachycardia. However, we are holding the following interpretations: First of all, we noticed that the earliest retrograde atrial activities were obviously different between F-S AVNRT and S-F AVNRT patients. In F-S patients, the earliest atrial activities were mostly (∼60%) at proximal coronary sinus (pCS), while the earliest atrial activities in S-F patients were predominantly (∼85%) appeared at the His region. It has been previously shown that different fibre connections exist between atria and AVN in typical S-F AVNRT resulting in a different retrograde atrial activation of the fast pathway.2 The different patterns of atrial activation may cause an alternate conduction time from AVN to atrium, and such a difference will potentially bring in more variable atrial intervals within each group that could influence interpreting the comparison of AH and HA interval further. Furthermore, it is also necessary to understand whether those patients with a similar atrial activity sequence are utilizing the same fast pathway during tachycardia. Hence, we are wondering whether it would be worthy to analyse the patients with an identical atrial activity in these two types of tachycardia separately, in other words, comparing the F-S and S-F types of patients with the earliest pCS atrial activity as well as comparing those with earliest His regional atrial activity. Secondly, the authors made the assumption that the conduction velocity over the fast pathway is similar in the anterograde and retrograde directions. However, it has previously been shown3 that the anterograde and retrograde conduction time of the AVN are different, which means that the Fretro and Fante would differ from each other even if they were using the same fast pathway. This could make the interpretation of the results more complicated, impacting both the estimated prevalence and mechanistic characterization of atypical AVNRT. Further challenges in characterizing atypical AVNRT remain, including difficulties associated with detailing the small area of Triangle of Koch and the direct recording of conduction or activity in the AVN. Nevertheless, the present study has provided new insight into the prevalence and mechanistic basis of atypical ANVRT which will be invaluable for its clinical interpretation moving forward.

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)0.002
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.013
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.005
Open science0.0010.001
Research integrity0.0130.011
Insufficient payload (model declined to judge)0.0020.002

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 head0.019
GPT teacher head0.272
Teacher spread0.253 · 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
GenreCommentary

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

Citations1
Published2015
Admission routes1
Has abstractyes

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