Evidence for Acute Myocardial and Skeletal Muscle Injury after Serial Transthoracic Shocks in Healthy Swine
Bibliographic record
Abstract
BACKGROUND: Previous serological studies have shown controversial results whether defibrillation or cardioversion can cause myocardial injury. Cardiovascular Magnetic Resonance (CMR) can be used to detect myocardial edema, hyperemia and capillary leak as features of acute myocardial injury. The aim of this study was to assess for myocardial and skeletal muscle injury in swine following transthoracic shocks. METHODS: Seventeen anaesthetized swine were examined, with 11 undergoing five synchronized transthoracic shocks (200J). Myocardial and skeletal muscle injury were assessed at baseline and up to 5h post-shock employing T1 mapping, T2 mapping, early and late gadolinium enhancement. Serologic markers (cFABP, TnI, CK, and CK-MB) and myocardial tissue were assessed by standard histology methods. RESULTS: In myocardial regions within the shock path, T1 and T2 were significantly increased compared to remote myocardium in the same animals. The early gadolinium enhancement ratio between the left-ventricular myocardium and the right pectoral muscle was also increased compared to control animals. After the shocks cFABP and CK were significantly elevated. After shock application, the regions identified as abnormal by CMR showed significantly increased interstitial and myocardial cell areas in histological analysis. This increased cell area suggests significant cellular and interstitial edema. CONCLUSION: Our pilot study data indicate that serial defibrillator shocks lead to acute skeletal muscle and myocardial injury. CMR is a useful tool to detect and localize myocardial and skeletal muscle injury early after transthoracic shocks in vivo. In the future the technique could potentially be used as an additional tool for quality control such as verifying insufficient local shock application in non-responders after cardioversion or to develop safer shock forms.
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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".