The Accuracy of Cardiac Surface Conduction Velocity Measurements
Bibliographic record
Abstract
Abstract Background: Conduction velocity (CV) is a measure of the health of myocardial tissue. It can be measured by taking differences in activation times from intracardiac electrodes. Several factors introduce error into the measurement, amongst which, ignoring the three dimensional aspect is a major detriment. The purpose of this paper was to determine if, nonetheless, there was a specific region where CV could be accurately measured. Methods: Computer simulations of three dimensional ventricles with a realistic His-Purkinje system were performed. Ventricles also included a dense scar or diffuse fibrosis. Results: A finer spatial sampling produced better agreement with true CV. Using a error limit of 10 cm/s as a threshold, measurements taken within a region less than 2 cm from the pacing site proved to be accurate. Error increased abruptly beyond this distance. The Purkinje system and tissue fibre orientation played equally major roles in leading to a surface CV which was not reflective of the CV propagation through the tissue. Conclusions: Surface CV measurements close to the pacing site taken with an electrode spacing of at most 1 mm, give reasonable estimates of tissue CV.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".