Experimental Investigation of Left Ventricular Flow Patterns After Percutaneous Edge‐to‐Edge Mitral Valve Repair
Bibliographic record
Abstract
Mitral valve percutaneous edge-to-edge repair (PEtER) is a viable solution in high-risk patients with severe symptomatic mitral regurgitation. However, the generated double-orifice configuration poses challenges for the evaluation of the hemodynamic performance of the mitral valve and may alter flow patterns in the left ventricle (LV) during diastole. This in vitro study aims to evaluate the hemodynamic modifications following a simulated PEtER. A custom-made mitral valve was developed, and two configurations were tested: (i) a single-orifice valve with mitral regurgitation and (ii) a double-orifice mitral valve configuration following PEtER. The hemodynamic performance of the valve was evaluated using Doppler echocardiography and catheterization, while the flow patterns in the LV were investigated using particle image velocimetry (PIV). The tests were run at a stroke volume of 65 mL and a heart rate of 70 bpm. PEtER was found to significantly reduce the regurgitant volume (15 vs. 34 mL). There was a good agreement between Doppler and catheter transmitral pressure gradients (peak gradient: 9 vs. 7 mm Hg; mean gradient: 4 vs. 3 mm Hg) as well as an excellent agreement between maximal velocity measured by Doppler and PIV (1.60 vs. 1.58 m/s). Vortex development in the LV during diastole was significantly different after repair. PEtER significantly increased the amplitude of Reynolds and viscous shear stresses, as well as the number of high shear regions in the LV, potentially promoting thromboembolism events.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".