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Diagnostic Pacing Maneuvers for Supraventricular Tachycardias: Part 2

2012· article· en· W1652793678 on OpenAlexaff
George D. Veenhuyzen, F. Russell Quinn, Stephen B. Wilton, Robin Clegg, L. Brent Mitchell

Bibliographic record

VenuePacing and Clinical Electrophysiology · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineSupraventricular tachycardiaCardiac pacingContext (archaeology)Electrophysiology studyCardiologyPaceVentricular pacingSupraventricular arrhythmiaTachycardiaAtrial fibrillationInternal medicineCatheter ablationHeart failure

Abstract

fetched live from OpenAlex

The approach to supraventricular tachycardia (SVT) diagnosis can be complex because it involves synthesizing baseline electrophysiologic features, features of the SVT, and the response(s) to pacing maneuvers. In this two-part review, we will mainly explore the latter while recognizing that neither of the former can be ignored, for they provide the context in which diagnostic pacing maneuvers must be correctly chosen and interpreted. Part 1 involved a detailed consideration of ventricular overdrive pacing, since this pacing maneuver provides the diagnosis in the majority of cases. In Part 2, other diagnostic pacing maneuvers that might be helpful when ventricular overdrive pacing is not diagnostic or appropriate, including attempts to reset SVT with single atrial or ventricular beats, para-Hisian pacing, apex versus base pacing, and atrial overdrive pacing, are discussed, as are some specific diagnostic SVT challenges encountered in the electrophysiology lab. There is considerable literature on this topic, and this review is by no means meant to be all-encompassing. Rather, we hope to clearly explain and illustrate the physiology, strengths, and weaknesses of what we consider to be the most important and commonly employed diagnostic pacing maneuvers, that is, those that trainees in cardiac electrophysiology should be well familiar with at a minimum.

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)0.001
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 score0.125
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.023
GPT teacher head0.345
Teacher spread0.322 · 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

Citations59
Published2012
Admission routes1
Has abstractyes

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