Abstract 191: Analysis Of Conduction Block During Ventricular Fibrillation In Langendorff Perfused Explanted Human Hearts.
Bibliographic record
Abstract
Purpose Rotors in Ventricular Fibrillation (VF) are known to occur in distinct domains identified by dominant frequency (DF). It has been postulated that VF is driven by these domains of excitation with fibrillatory propagation to adjacent domains with conduction blocks. If the dominant domain hypothesis is applicable to human VF, we postulated that minimally during VF, areas blocks in a DF map should localize at boundaries of DF domains. Methods Explanted hearts from 4 patients undergoing cardiac transplant were perfused with Krebs-Henseilet solution. The epicardial and endocardial mapping was constructed with 112 electrodes disposed on an extensible sock and balloon array. The electrodes were organized in 14 rows of 8 electrodes disposed radially around the apex. 12 episodes of VF from the 4 hearts were analyzed. Fast Fourier Transformation (FFT) analysis was performed with a resolution of 0.25 Hz. DF domain was defined as a region on the DF map with uniform DF. Double peak (DP) an estimate of conduction block, was identified at a time point if a secondary peak was present in the power spectrum of an FFT that was within a maximum separation of 80% of the dominant (highest) peak frequency and that had an amplitude of at least 20% of the amplitude of the dominant peak. Results The average DF was 4.64±1.20 Hz. The mean percentage of blocks was 26.13±12.19 %. The average number of domains per DF map were 9.25±3.77. The mean percent block per domain was 2.95±1.27.The number of domains per map correlated significantly to areas with blocks. (r = 0.49, p=0.014). When the hearts were individually analyzed, the correlation was greater than when analyzed together. Higher occurrence of block is seen with increasing number of DF domains. Conclusions During human VF, areas of blocks in a DF map localize at boundaries of DF domains.
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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 | 0.000 | 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) | 0.001 | 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".