Relating spatial heterogeneities to rotor formation in studying human ventricular fibrillation
Bibliographic record
Abstract
Ventricular fibrillation (VF) occurs due to disorganized electrical activity in the ventricles. This leads to rapid uncoordinated contractions of the ventricles and sudden cardiac death if not treated within minutes of its occurrence. The mechanism of VF initiation and maintenance is still elusive, however the mother rotor and multiple wavelet theories attempt to explain the mechanism behind this lethal arrhythmia. In mother rotor theory, VF is believed to be maintained by high frequency periodic sources called rotors that could be tracked using the phase progression along and through the myocardium using spatio-temporal electrical mapping of the heart. There are exiting works including our previous works that have related the formation of these rotors to anatomical and physiological heterogeneities observed in the myocardium. In this study we performed an correlation exercise of the locations of rotors with scar boundary maps and dominant frequency maps and elucidated this relation using human VF data acquired from isolated human hearts. The results suggest that in 14 rotors over 6 human hearts that we studied, all rotors co-localized to boundary zones of scar and low-high dominant frequency locations. The mean variance of the dominant frequency over the spatial location of the rotor was found to be 0.55 with average minimum of 4.15 Hz to a maximum of 5.71 Hz. This results in human VF data strongly suggest that boundary zones of healthy-non-healthy tissues and low-high frequency boundaries form a favorite substrate for rotor formation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 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".