Experimental investigation of the flow downstream of a dysfunctional bileaflet mechanical aortic valve
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
Mechanical heart valve replacement is the preferred alternative in younger patients with severe symptomatic aortic valve disease. However, thrombus and pannus formations are common complications associated with bileaflet mechanical heart valves. This leads to risks of valve leaflet dysfunction, a life-threatening event. In this experimental study, we investigate, using time-resolved planar particle image velocimetry, the flow characteristics in the ascending aorta in the presence of a dysfunctional bileaflet mechanical heart valve. Several configurations of leaflet dysfunction are investigated and the induced flow disturbances in terms of velocity fields, viscous energy dissipation, wall shear stress, and accumulation of viscous shear stresses are evaluated. We also explore the ability of a new set of parameters, solely based on the analysis of the normalized axial velocity profiles in the ascending aorta, to detect bileaflet mechanical heart valve dysfunction and differentiate between the different configurations tested in this study. Our results show that a bileaflet mechanical heart valve dysfunction leads to a complex spectrum of flow disturbances with each flow characteristic evaluated having its own worst case scenario in terms of dysfunction configuration. We also show that the suggested approach based on the analysis of the normalized axial velocity profiles in the ascending aorta has the potential to clearly discriminate not only between normal and dysfunctional bilealfet heart valves but also between the different leaflet dysfunction configurations. This approach could be easily implemented using phase-contrast MRI to follow up patients with bileaflet mechanical heart valves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 it