A novel computational model for the hemodynamics of bileaflet mechanical valves in the opening phase
Bibliographic record
Abstract
A powerful alternative means to study the hemodynamics of bileaflet mechanical heart valves is the computational fluid dynamics method. It is well recognized that computational fluid dynamics allows reliable physiological blood flow simulation and measurements of flow parameters. To date, in almost all of the modeling studies on the hemodynamics of bileaflet mechanical heart valves, a velocity (mass flow)-based boundary condition and an axisymmetric geometry for the aortic root have been assigned, which, to some extent, are erroneous. Also, there have been contradictory reports of the profile of velocity in downstream of leaflets, that is, in some studies, it is suggested that the maximum blood velocity occurs in the lateral orifice, and in some other studies, it is postulated that the maximum velocities in the main and lateral orifices are identical. The reported values for the peak velocities range from 1 to 3 m/s, which highly depend on the model assumptions. The objective of this study is to demonstrate the importance of the exact anatomical model of the aortic root and the realistic boundary conditions in the hemodynamics of the bileaflet mechanical heart valves. The model considered in this study is based on the St Jude Medical valve in a novel modeling platform. Through a more realistic geometrical model for the aortic root and the St Jude Medical valve, we have developed a new set of boundary conditions in order to be used for the assessment of the hemodynamics of aortic bileaflet mechanical heart valves. The results of this study are significant for the design improvement of conventional bileaflet mechanical heart valves and for the design of the next generation of prosthetic valves.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".