Studying semblances of a true killer: experimental model of human ventricular fibrillation
Bibliographic record
Abstract
It is unknown whether ventricular fibrillation (VF) studied in experimental models represents in vivo human VF. First, we examined closed chest in vivo VF induced at defibrillation threshold testing (DFT) in four patients with ischemic cardiomyopathy pretransplantation. We examined VF in these same four hearts in an ex vivo human Langendorff posttransplantation. VF from DFT was compared with VF from the electrodes from a similar region in the right ventricular endocardium in the Langendorff using two parameters: the scale distribution width (extracted from continuous wavelet transform) and VF mean cycle length (CL). In a second substudy group where multielectrode phase mapping could be performed, we examined early VF intraoperatively (in vivo open chest condition) in three patients with left ventricular cardiomyopathy. We investigated early VF in the hearts of three patients in an ex vivo Langendorff and compared findings with intraoperative VF using two metrics: dominant frequency (DF) assessed by the Welch periodogram and the number of phase singularities (lasting >480 ms). Wavelet analysis (P = 0.9) and VF CL were similar between the Langendorff and the DFT groups (225 ± 13, 218 ± 24 ms; P = 0.9), indicating that wave characteristics and activation rate of VF was comparable between the two models. Intraoperative DF was slower but comparable with the Langendorff DF over the endocardium (4.6 ± 0.1, 5.0 ± 0.4 Hz; P = 0.9) and the epicardium (4.5 ± 0.2, 5.2 ± 0.4 Hz; P = 0.9). Endocardial phase singularity number (9.6 ± 5, 12.1 ± 1; P = 0.6) was lesser in number but comparable between in vivo and ex vivo VF. VF dynamics in the limited experimental human studies approximates human in vivo VF.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".