Value of the Signal-Averaged ECG in Supraventricular Tachycardia Diagnosis
Bibliographic record
Abstract
Background: We hypothesized that signal averaged ECG during tachycardia would facilitate P wave recognition and assist in SVT diagnosis. P waves adjacent to the QRS during tachycardia would lengthen the filtered QRS and be recognized by subtracting QRS duration during sinus rhythm from that of tachycardia. Aims: to assess the feasibility of SaECG during SVT; to correlate the difference between the filtered QRS duration in SVT and sinus rhythm (ΔfQRSd) with the endocardial VA time; Methods & Results: Patients referred for an EP study and ablation of any SVT were included. A SAECG was acquired during SVT and compared with another during SR. 40 patients were included, 20 had AVNRT and 20 AVRT. For AVNRT, the P wave was detected as a pseudo-late potential in 16 patients. In 4 patients, P wave was invisible and presumed within the confines of the QRS. The mean ΔfQRSd was 2017 ms and the VA time was 1415 ms. For AVRT a distinct P wave separated from the QRS was detected in all patients. The ΔfQRSd was 10742ms and the VA time was 9631 ms. ΔfQRSd was longer during AVRT than AVNRT (p<0.0001). Over all, the ΔfQRSd correlated with the longest VA time (R=0.796). Motion artifact and sensing of T waves during tachycardia were 2 confounders. Conclusion: SaECG provides a rapid adjunct to the 12 lead ECG and is capable of identifying P waves and facilitating diagnosis of SVT mechanism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".