Dipole Density Mapping of Atrial Fibrillation
Bibliographic record
Abstract
An ElectroFunctional Procedure breaks down the Complexity Non-contact, voltage-based surface electrograms underpin the electrocardiogram and when planning treatment strategies in complex arrhythmias, electrophysiologists rely on 2D and 3D displays of voltage-based, intracardiac electrograms. Whether derived through contact or non-contact, voltage-based signal analysis is fundamental to cardiology practice. At the physiological level, voltage is the measure of an electric field that includes both the local activation source and the sum of the surrounding activation sources. Accordingly, voltage-based mapping systems record the broad-and-smooth nature of voltage with the electrogram displaying a blended view of cardiac activity that may obscure the details of complex rhythm patterns. Dipole density mapping, as an alternative, represents only the local charge-sources on the interior surface of the chamber, resulting in a sharp-and-narrow delineation of cardiac activity (Figure1). ... The AcQMap imaging and mapping system rapidly creates highly accurate ultrasound-based heart chamber reconstructions combined with high resolution maps of electrical conduction using dipole density instead of voltage.1,2. This novel technology utilizes non-contact sensors that enable a continuous global view of the conduction of each heartbeat and permits mapping of any cardiac arrhythmia. The diagnostic recording AcQMap catheter is deployed by the user into a spheroid-shape comprising six splines each populated with eight ultrasound transducers and eight engineered electrodes (Figure 2A).
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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 | 0.001 | 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".