Diagnostic Pacing Maneuvers for Supraventricular Tachycardias: Part 2
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".