Choice and Utility of Pacing Maneuver in Establishing the Mechanism of Supraventricular Tachycardia: A Single Center Experience
Bibliographic record
Abstract
BACKGROUND: To evaluate the choice and utility of pacing maneuvers in the electrophysiology (EP) laboratory in establishing supraventricular tachycardia (SVT) mechanism. METHODS: We retrospectively examined a cohort of 160 consecutive patients with SVT presenting for invasive EP evaluation to a single center with 8 electrophysiologists. We analyzed the utility of the two most commonly used pacing maneuvers: (1) ventricular entrainment (VE) and (2) His-refractory premature ventricular stimuli (HRPVC) during SVT. RESULTS: VE was performed in 96 patients: atrial tachycardia (AT) 12, atrioventricular nodal reentrant tachycardia (AVNRT) 66, and orthodromic reciprocating tachycardia (ORT) 18. During VE, AT patients were most likely to have ventriculo-atrial (VA) dissociation (AT 58%, AVNRT 18%, ORT 0%, P < 0.001) and had a tendency towards less SVT termination (AT 0%, AVNRT 9%, ORT 11%, P = 0.19). HRPVCs were delivered in 39 patients: AT 1, AVNRT 24, and ORT 14. Advancement of atrial signal with HRPVC was only observed in ORT (AT 0%, AVNRT 0%, ORT 79%, P < 0.001) and SVT termination was also mostly observed in ORT (AT 0%, AVNRT 4%, ORT 21%, P = 0.33). The overall diagnostic utility of VE was lowest in AT (AT 42%, AVNRT 71%, ORT 83%, P = 0.04), while HRPVC was rarely used in AT. Furthermore, the utilization of maneuvers varied extensively (0% to100%) among the 8 electrophysiologists. CONCLUSION: There is great variation in the utilization of pacing maneuvers and their utility in ascertaining the mechanism of SVT. Our results support the fact that discerning AT from AVNRT mechanism remains the most challenging task in SVT diagnosis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".