High‐Resolution Analysis of the Surface P Wave as a Measure of Atrial Electrophysiological Substrate
Bibliographic record
Abstract
BACKGROUND: At present atrial electrophysiology can only be assessed by invasive study. This limits available data in humans concerning atrial electrophysiologic changes in disease and in response to intervention. Indirect evidence suggests that the signal-averaged P wave (SAPW) may provide noninvasive markers of atrial electrophysiology but no direct evaluations that measure both refractoriness and conduction time have been reported. METHODS: We investigated 9 patients attending for diagnostic electrophysiological studies (4 male; mean age 35.7 years). A 20-pole catheter was positioned in the right atrium; a decapole catheter was placed in the coronary sinus. Atrial effective refractory period (AERP) and conduction times were measured at the lateral and septal right atrium and the left atrium during sinus rhythm (SR) and at pacing cycle lengths of 600, 500, and 400 ms. Simultaneous SAPW recordings were taken during SR and pacing at 600 ms. Intravenous flecainide (2 mg/kg) was given after which the protocol was repeated. RESULTS: Flecainide slowed conduction significantly at all sites (P < 0.05). During baseline measurements, rate adaptation of AERP was observed (P < 0.02 at the septum). Flecainide increased filtered P wave duration (P < 0.05) and reduced P wave energies (P < 0.05). Negative correlation was observed between P wave energies and conduction time with an inverse relationship between high-frequency energy and left atrial AERP. CONCLUSIONS: The SAPW provides a noninvasive marker of atrial electrophysiology.
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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 | 0.001 |
| 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) | 0.002 | 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".