Flow through a defective mechanical heart valve: A steady flow analysis
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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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".