The use of adenosine to identify dormant conduction after accessory pathway ablation: a single center experience and literature review.
Bibliographic record
Abstract
INTRODUCTION: Atrio-ventricular reentrant tachycardias (AVRT) represent around 40 percent of supraventricular tachycardias. After ablation, recurrence rates are around 10 percent. Adenosine has been described as a useful tool to assess presence of dormant conduction and predict recurrence after apparently successful ablation. We reviewed the patients of our service and assessed the role of adenosine in predicting dormant conduction and factors that could influence recurrence rates. METHODS: We retrospectively reviewed electrophysiologic studies and medical charts of 65 patients who had AVRT ablation and had adenosine used to assess dormant conduction at a single quaternary center between 2011 and 2015. Dormant conduction was defined as transient recovery of the preexcitation (for pathways with antegrade conduction) or return of the retrograde conduction through an apparently successfully ablated concealed accessory pathway (AP). RESULTS: One patient was found to have dormant conduction (1.5%) with early recurrence that was not further ablated due to the difficult location of the AP. The overall recurrence rate was 4.6%. General features like location of AP's, their properties, ablation times and technique were assessed. CONCLUSION: Similar to its use in identifying other arrhythmias, adenosine may be useful in identifying dormant conduction for further ablation during initial ablation of an accessory pathway; however, the absence of dormant conduction on adenosine testing does not reliably predict non-recurrence. The low recurrence rates in our service may be related to the frequent use of irrigated tip catheters, 3D mapping and long average ablation time over the successful site of ablation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".