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 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 | 0.007 |
| 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 | 0.001 |
| 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".