Left Ventricular Rotation and Torsion by Speckle Tracking Echocardiography During Semi-Supine Cycle Exercise in Children
Bibliographic record
Abstract
During the cardiac cycle the heart undergoes a wringing motion referred to as torsion. Normal torsion is important for optimal systolic and diastolic function of the heart. Speckle tracking imaging has recently become a useful, non-invasive tool for determining left ventricular (LV) torsion. This angle-independent method is thought to be highly reproducible and correlates well with MRI measurements. Changes in LV torsion during exercise can be used to assess myocardial function. PURPOSE: We sought to determine the feasibility of measuring rotation and torsion in controls (CON) and pediatric transplant patients (PT) during incremental semi-supine cycle exercise. METHODS: Fourteen CON (median age: 11.1 years) and 5 PT (median age: 14.8 years) exercised to volitional fatigue. 2D echo basal and apical short axis views were obtained at rest, at each stage of cycle exercise, immediately- and 3 minutes post-exercise. Rotation and torsion were obtained by standard techniques. Each variable was measured over three cardiac cycles and averaged. Coefficients of variation (CV) were calculated. RESULTS: Data acquisition was increasingly difficult with increasing exercise intensity. At peak exercise, it was possible to obtain data in only 5/14 CON and 2/5 PT; however, sub-maximal exercise data could be obtained in 11/14 CON and 5/5 PT and immediately post-exercise data in 9/14 CON and 5/5 PT. The CV were as high as 50%. CONCLUSIONS: This preliminary study shows that measurement of rotation and torsion is feasible at rest and during sub-maximal exercise in children, but difficult to measure with increasing exercise intensity during semi-supine cycle exercise. Failure to document an increase in rotation and torsion during exercise may reflect the technical difficulties of this method and individual measurement variability.Table: No title available.
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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 | 0.002 |
| 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 | 0.000 |
| 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".