Abstract 10693: Noninvasive Assessment of Contractility and Compliance of the Left Ventricle in Children: Myocardial Stiffness Measured Throughout the Cardiac Cycle by Shear Wave Imaging
Bibliographic record
Abstract
Background: Intrinsic myocardial stiffness (MS) is load-independent, unlike most parameters to assess LV systolic and diastolic function. Pre-clinical studies have found good correlation between 1) peak-systolic MS and contractility (or ESPVR); 2) end-diastolic MS and compliance (or EDPVR). Shear wave imaging (SWI) by ultrafast ultrasound imaging allows MS measurement throughout the cardiac cycle. Methods (Fig 1): Five children with LV pressure-overload (LV-PO; 2 aortic stenosis, 3 coarctation) were studied during cardiac catheterization and there were 5 age-matched controls. Interventricular septal MS was measured by SWI every 100ms during the cardiac cycle, pre- and post-balloon dilation. ESPVR and EDPVR were assessed by pressure-volume loops by a pressure catheter and real-time 3D-echo volumes. Results (Fig 2): Peak-systolic MS was higher in LV-PO (22.7±6.7 kPa) than controls (8.4±2.8 kPa; p<0.01) and compared to end-diastolic MS (LV-PO 4.8±1.7 kPa, control 1.3±0.8 kPa; p<0.01). End-diastolic MS was correlated with LV end-diastolic pressure (r=0.74). Relief of PO did not change the peak MS in the LV-PO (p=0.88). Correlations between peak-systolic MS and ESPVR (r=0.72), and between end-diastolic MS and EDPVR (r=0.79) were confirmed. Conclusions: Non-invasive MS is a load-independent measure of LV systolic contractility and diastolic compliance in children.
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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 | 0.001 | 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) | 0.004 | 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".