Paced P wave morphology templates to guide atrial tachycardia localization
Bibliographic record
Abstract
Introduction: Surface ECG is a useful tool to guide mapping of focal atrial tachycardia (AT).We thought to construct an algorithm, based on paced P wave templates from different anatomical sites in both atria in patients with normal hearts. Methods: We prospectively enrolled consecutive patients who underwent electrophysiology study, having no heart disease. Atrial pacing was carried out at different anatomical sites in both atria. Paced P wave morphology (PWM) and duration (PWD) were assessed. P wave morphology was classified into: positive, negative, biphasic (+/- or -/+) and isoelectric. A proposed algorithm was generated from the constructed templates of each pacing site. Results: Sixty-four patients (25 males) were enrolled. Mean age was 37 ± 13 years. Atrial pacing was performed in 61 patients (95%) at the right atrium and in 15 patients (23%) at the left atrium. A neg/iso P wave in V1, a pos/iso P wave in AVL and lead I identified right atrial pacing sites (p=0.01, p=0.02 and p=0.02, respectively). Negative P wave in lead aVL identified left pulmonary veins when compared to right pulmonary veins (P=0.03). PWD was significantly longer when pacing from lateral tricuspid annulus (TA) as compared to medial TA (136 ± 12ms vs. 99 ± 10 ms , P=<0.001) and when pacing from the left superior pulmonary vein as compared to the right superior pulmonary vein (152 ± 12 ms vs. 135± 10 ms, (P=0.001). Conclusions: PWM and PWD derived from templates generated through atrial pacemapping could be used to guide localization of focal AT.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".