Feature tracking strain analysis detects the onset of regional diastolic dysfunction in territories with acute myocardial injury induced by transthoracic electrical interventions
Bibliographic record
Abstract
Electric interventions are used to terminate arrhythmia. However, myocardial injury from the electrical intervention can follow unique pathways and it is unknown how this affects regional ventricular function. This study investigated the impact of transthoracic electrical shocks on systolic and diastolic myocardial deformation. Ten healthy anaesthetized female swine received five transthoracic shocks (5 × 200 J) and six controls underwent a cardiovascular magnetic resonance exam prior to and 5 h after the intervention. Serial transthoracic shocks led to a global reduction in both left (LV, - 15.6 ± 3.3% to - 13.0 ± 3.6%, p < 0.01) and right ventricular (RV, - 16.1 ± 2.3% to - 12.8 ± 4.2%, p = 0.03) peak circumferential strain as a marker of systolic function and to a decrease in LV early diastolic strain rate (1.19 ± 0.35/s to 0.95 ± 0.37/s, p = 0.02), assessed by feature tracking analysis. The extent of myocardial edema (ΔT1) was related to an aggravation of regional LV and RV diastolic dysfunction, whereas only RV systolic function was regionally associated with an increase in T1. In conclusion, serial transthoracic shocks in a healthy swine model attenuate biventricular systolic function, but it is the acute development of regional diastolic dysfunction that is associated with the onset of colocalized myocardial edema.
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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) | 0.000 | 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 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".