Measurement of thoracic current flow in pigs for the study of defibrillation and cardioversion
Bibliographic record
Abstract
Although defibrillation has been in clinical use for more than 50 years, the complete current flow distribution inside the body during a defibrillation procedure has never been directly measured. This is due to the lack of appropriate imaging technology to noninvasively monitor the current flow inside the body. The current density imaging (CDI) technique, using a magnetic resonance (MR) imager, provides a new approach to this problem [Scott et al. (1991)]. CDI measures the local magnetic field generated by the current and calculates the current density by computing its curl. In this study, CDI was used to measure current density at all points within a postmortem pig torso during an electrical current application through defibrillation electrodes. Furthermore, current flow information was visualized along the chest wall and within the chest cavity using streamline analysis. As expected, some of the highest current densities were observed in the chest wall. However, current density distribution varied significantly from one region to another, possibly reflecting underlying heterogeneous tissue conductivity and anisotropy. Moreover, the current flow analysis revealed many complex and unexpected current flow patterns that have never been observed before. This study has, for the first time, noninvasively measured the volume current measurement inside the pig torso.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".