Clinical significance of conduction velocity orientation vectors on cardiac mapping surfaces
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): This work was supported by the Lefoulon Delalande Foundation OBJECTIVE Electroanatomical maps using automated conduction velocity (CV) algorithms are now being calculated using 2D mapping tools. The basis of substrate-based functional assessment is to detect slow zones or slow CV of which isochronal late activation mapping (ILAM) is now commonly performed. We studied the accuracy of mapping surface 2D CV, compared to the 3D vectors, and the influence of mapping resolution in non-scarred animal and human heart models. Methods Two models were used: a healthy porcine Langendorff model with transmural needle electrodes and a computer stimulation model of the ventricles built from an MRI-segmented, excised human heart. Local activation times (LAT) within the 3D volume of the mesh were used to calculate true 3D CVs (direction and velocity) for different pixel resolutions ranging between 500 mm – 4 mm (3D CVs). CV was also calculated for endocardial surface-only LATs (2D CV). Results In the experimental model, surface (2D) CV was faster on the epicardium (0.509 m/s) compared to the endocardium (0.262 m/s, Figure 1). In stimulation models, 2D CV significantly exceeded 3D CVs across all mapping resolutions and increased as resolution decreased (Figure 2). 3D and 2D left ventricle CV at 500mm resolution increased from 429.2±189.3 mm/s to 527.7±253.8 mm/s (P<0.01), respectively with modest correlation (R=0.64). Decreasing the resolution to 4mm significantly increased 2D CV and weakened the correlation (R=0.46). The majority of CV vectors were not parallel (<30°) to the mapping surface providing a potential mechanistic explanation for erroneous LAT-based CV over-estimation. Conclusion Ventricular CV is overestimated when using 2D LAT-based CV calculation of the mapping surface and is significantly compounded by mapping resolution. These findings have implications for the current methodology of functional substrate assessment with ILAM and surface CV assessment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".