Signal‐Averaged P Wave Reflects Change in Atrial Electrophysiological Substrate Afforded by Verapamil Following Cardioversion from Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Detailed analysis of signal-averaged P waves (SAPW) can provide insights into atrial electrophysiology. Abbreviated dosing of verapamil prior to cardioversion improves outcome at 1 week postcardioversion. The mechanism by which verapamil manifests benefit is uncertain. We hypothesized the SAPW would reflect any change in atrial electrophysiologic substrate afforded by verapamil when compared with controls. METHODS: We investigated 23 patients attending external cardioversion of persistent atrial fibrillation (AF) (6 female; mean age 68 years). Patients were randomized to verapamil 240 mg daily in three divided doses 3 days before cardioversion and 1 week after, or usual medication. SAPW recordings were performed during sinus rhythm (SR) immediately after cardioversion, at 24 hours and 1 week. RESULTS: The groups were comparable in terms of age, gender, left atrial size, and duration of AF. Eight of nine patients prescribed verapamil maintained SR at 1 week postcardioversion compared with 6 of 14 controls (P = 0.027). SAPW spectral analysis delivered higher energy for patients prescribed verapamil (median (IQ range)); 40.8 (33.4-95.1) versus 25.7 (19.0-38.0) for energy within 20-150 Hz, P20 (microV(2)x s; P = 0.03). There was no difference in P-wave duration (PWD) or root mean square of the terminal 30 ms between the two groups. Early reinitiation occurred in patients with significantly lower P-wave energy 19.6 (12.9-24.6) versus 39.9 (24.0-47.0) (P = 0.017). CONCLUSIONS: Verapamil 240 mg daily for 3 days prior to cardioversion and 1 week after reduces early recurrence of AF. The SAPW observations indicate change in atrial electrophysiologic substrate might be responsible for benefit afforded by verapamil.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".