Abnormal Atrial Potentials Recorded During Sinus Rhythm or Pacing Represent Substrates for Reentrant Atrial Tachycardia
Bibliographic record
Abstract
BACKGROUND: Abnormal atrial potentials (AAPs) recorded during sinus rhythm/atrial pacing may indicate areas of slow conduction capable of supporting reentrant atrial tachycardia (AT). Therefore, we sought to examine the relationship between AAPs and AT circuits. METHODS: One hundred twenty-three reentrant ATs in 104 patients were analyzed. AAPs, consisting of fragmented potentials and split potentials, were assessed using the Rhythmia LUMIPOINT algorithm. RESULTS: There was 93±13% overlap between areas with AAPs during sinus rhythm/atrial pacing and areas of slow conduction along the reentry circuit during AT. The cumulative area of AAPs was smaller in patients with localized-reentrant ATs compared with anatomic macro-reentrant ATs (20.0 [14.6–30.5] versus 28.9 [21.8–35.6] cm 2 ; P =0.021). Patients with perimitral ATs had larger areas of AAPs on the lateral wall whereas patients with roof-dependent ATs had larger areas of AAPs on the roof and posterior wall ( P ≤0.018 for all comparisons). The patchy scar that was associated with localized-reentrant AT exhibited a larger area of AAPs at its periphery than the scar that did not participate in localized-reentrant AT (3.1 [2.4–4.5] versus 1.0 [0.7–1.6] cm 2 ; P <0.001). CONCLUSIONS: AAPs recorded during sinus rhythm/atrial pacing are associated with areas of slow conduction during reentrant AT. The burden and distribution of AAPs may provide actionable insights into AT circuit features, including in cases in which ATs are difficult to map.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".