Bibliographic record
Abstract
The aim of this study is to determine if some of the characteristics of reconstructed unipolar electrograms from the noncontact mapping system can be used to detect epicardial and to differentiate it from endocardial electrical activation in a canine heart. This would help the electrophysiologist know where exactly the origin of ventricular tachycardia or the critical point in tissue is located. Following this, arrhythmia can be successfully treated by ablating that part of the tissue of the heart. Virtual electrograms were recorded while pacing the right ventricle of an open-chest dog at multiple endocardial and epicardial sites using the commercially available noncontact mapping system (EnSite Array™ Catheter 3000). The endocardial and epicardial paced virtual electrograms from the juxtaposing sites allow for analyzing systematically the differences in their morphologies. Maximal dV / dt, area under the depolarization curve and latency extracted from unipolar electrograms demonstrated significant difference between epicardial and endocardial pacing sites with a p-value of less than 0.01 in all three cases. The above features were fed to a linear discriminant analysis based classifier and high classification accuracy was achieved. Therefore, reliable criteria can be proposed to allow for discrimination of an endocardial versus epicardial origin of electrical activation. And also the endocardial and epicardial paced virtual electrograms from the juxtaposing sites allows for an estimate of the transfer function of the myocardium in different positions of the right ventricles of a canine heart. The transfer function estimation will aid in better mathematical modeling of myocardium and could be a sensitive measure of myocardial homogeneity and arrhythmic foci localization.Another study was done on a human heart. This study was to evaluate the ability of virtual electrograms to predict abnormal bipolar electrograms. We tested the hypothesis of maxdV/dt, filtering and optimized DSM threshold. This allows better identification of abnormal myocardial substrate traditionally defined by contact bipolar mapping in human RVOT.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".