Orientation of conduction velocity vectors on cardiac mapping surfaces
Bibliographic record
Abstract
AIMS: Electroanatomical maps using automated conduction velocity (CV) algorithms are now being calculated using two-dimensional (2D) mapping tools. We studied the accuracy of mapping surface 2D CV, compared to the three-dimensional (3D) vectors, and the influence of mapping resolution in non-scarred animal and human heart models. METHODS AND RESULTS: 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 (LATs) within the 3D volume of the mesh were used to calculate true 3D CVs (direction and velocity) for different pixel resolutions ranging between 500 μm and 4 mm (3D CVs). CV was also calculated for endocardial surface-only LATs (2D CV). In the experimental model, surface (2D) CV was faster on the epicardium (0.509 m/s) compared to the endocardium (0.262 m/s). In stimulation models, 2D CV significantly exceeded 3D CVs across all mapping resolutions and increased as resolution decreased. Three-dimensional and 2D left ventricle CV at 500 μm resolution increased from 429.2 ± 189.3 to 527.7 ± 253.8 mm/s (P < 0.01), respectively, with modest correlation (R = 0.64). Decreasing the resolution to 4 mm 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 significantly compounded by mapping resolution. Three-dimensional electric field-based approaches are needed in mapping true CV on mapping surfaces.
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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 | 0.005 |
| 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 | 0.000 |
| 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".