The Gurvich waveform has lower defibrillation threshold than the rectilinear waveform and the truncated exponential waveform in the rabbit heart
Bibliographic record
Abstract
Implantable cardioverter defibrillator studies have established the superiority of biphasic waveforms over monophasic waveforms. However, external defibrillator studies of biphasic waveforms are not as widespread. Our objective was to compare the defibrillation efficacy of clinically used biphasic waveforms, i.e., truncated exponential, rectilinear, and quasi-sinusoidal (Gurvich) waveforms in a fibrillating heart model. Langendorff-perfused rabbit hearts (n = 10) were stained with a voltage-sensitive fluorescent dye, Di-4-ANEPPS. Transmembrane action potentials were optically mapped from the anterior epicardium. We found that the Gurvich waveform was significantly superior (p < 0.05) to the rectilinear and truncated exponential waveforms. The defibrillation thresholds (mean +/- SE) were as follows: Gurvich, 0.25 +/- 0.01 J; rectilinear-1, 0.34 +/- 0.01 J; rectilinear-2, 0.33 +/- 0.01 J; and truncated exponential, 0.32 +/- 0.02 J. Using optically recorded transmembrane responses, we determined the shock-response transfer function, which allowed us to predict the cellular response to waveforms at high accuracy. The passive parallel resistor-capacitor model (RC-model) predicted polarization superiority of the Gurvich waveform in the myocardium with a membrane time constant (taum) of less than 2 ms. The finding of a lower defibrillation threshold with the Gurvich waveform in an in vitro model of external defibrillation suggests that the Gurvich waveform may be important for future external defibrillator designs.
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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 | 0.001 |
| 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 | 0.000 |
| 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".