3D pulsatile flow in a curved tube with coexisting model of aortic stenosis and coarctation of the aorta
Bibliographic record
Abstract
Coarctation of the aorta is a congenital heart disease defined as an obstruction of the aorta distal to the left subclavian artery (between the aortic arch and descending aorta). It is usually associated with other diseases such as bicuspid and tricuspid aortic stenosis. If the coarctation remains uncorrected it can lead to hypertension, left ventricular failure and aortic dissection. Numerous investigations pointed out that there is a relationship between the genesis and the progression of cardiovascular disease and the locally irregular flow occurring at the diseased zone. Therefore, to examine the relationship between arterial disease and hemodynamics conditions, detailed quantitative studies on flow dynamics in arterial models are clearly inquired. In this study we numerically investigate pulsatile blood flow in a simplified model of the aorta (curved pipe) with coexisting coarctation of the aorta and aortic stenosis. Three severities of aortic stenoses (0.61 cm(2), 1.0 cm(2) and 1.5 cm(2)) coexisting with aortic coarctations (50%, 75% and 90% by area) are investigated. An experimentally validated numerical model from literature is used and baseline results are validated against it. To ensure having a physiologically relevant model using this geometry, flow properties are set so that the Dean number falls in the physiological range for the aorta. The results show that the coexistence of these pathologies significantly modifies the flow in a curved pipe. The maximal velocity is shifted towards the outer wall and can reach values as high as 5m/s just downstream of the coarctation. The wall shear stress distribution is significantly modified compared to the normal, unobstructed case. Finally, a clinically significant pressure gradient is induced by the curvature of the tube (up to 36 mmHg). This can lead to an overestimation of the severity of the coarctation using catheterization.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".