Abstract 4666: Anatomic Substrate as Determinant of Dominant Frequency Dynamics During Human Ventricular Fibrillation
Bibliographic record
Abstract
Background: Dominant frequency (DF) analysis is a common way of quantitatively studying the spatio-temporal variation of frequency during VF. Areas of high frequencies (e.g. rotors) and low frequencies (e.g. blocks) have been associated with the occurrence and maintenance of VF. However, the relation of these high or low frequency areas to anatomical or physiological substrate remains unclear. Objective: We tested the hypothesis that the Max-Min DF locations in the epicardium during VF are due to anatomical substrate. Methods and Results: We analyzed 33, 4 seconds VF episodes acquired from 6 isolated human hearts using a Langendorff setup. The hearts were received from the heart-transplanted patients with informed consent. Electrode array consisting 112 bipolar electrodes was used to acquire the surface unipolar and bipolar electrograms from the epicardium. DF was computed as the peak frequency of the VF segment from each of electrodes using Welch’s modified periodogram method. From the DF distribution, the regions of max and min frequencies were identified for LV and RV regions. Scar maps were computed for each of the 6 hearts using a previously published method by mapping the amplitude of bipolar electrograms (<0.5mv = scar) during the pacing protocol. The areas of max-min DF frequencies in each of the VF episode were compared to the corresponding spatial locations in the scar map. Table 1 shows the match between the max-min DF frequency locations and the scar locations. 50% of max-min DF frequencies locations match the scar locations and in 97% of the matched locations the max-min DF occur at the vicinity of the scar. Conclusion: During human VF, DF dynamics are only partially explained by the anatomical substrate. This suggests that ion channel heterogeneity and dynamic physiological factors may play an important role in determining fibrillation dynamics. Match Between Max-Min DF locations and Scar Locations
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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".