Flow through a defective mechanical heart valve: A steady flow analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Approximately 250,000 valve replacement operations occur annually around the world and more than two thirds of these operations use mechanical heart valves (MHV). These valves are subject to complications such: pannus and/or thrombus formation. Another potential complication is a malfunction in one of the valve leaflets. Although the occurrence of such malfunctions is low, they are life-threatening events that require emergency surgery. It is, therefore, important to develop parameters that will allow an early non-invasive diagnosis of such valve malfunction. In the present study, we performed numerical simulations of the flow through a defective mechanical valve under several flow and malfunction severity conditions. Our results show that the flow upstream and downstream of the defective valve is highly influenced by malfunction severity and this resulted in a misleading improvement in the correlation between simulated Doppler echocardiographic and catheter transvalvular pressure gradients. In this study, we were also able to propose and test two potential non-invasive parameters, using Doppler echocardiography and phase contrast magnetic resonance imaging, for an early detection of mechanical heart valve malfunction. Finally, we showed that valve malfunction has a significant impact on platelet activation and therefore on thrombus formation.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it