Hemodynamic characterization of aortic stenosis states
Bibliographic record
Abstract
Abstract Aortic stenosis (AS) has become an increasingly prevalent clinical condition, as a result of the “greying of the population”, the widespread application of sophisticated diagnostic tools including non‐invasive imaging and invasive techniques, and the advent of minimally invasive surgical and percutaneous valve therapies. The diagnosis of severe AS traditionally has relied on the assessment of the mean transvalvular gradient (Δ P mean ) and aortic valve area (AVA) by either echocardiography or catheterization. However, other hemodynamic variables as flow, pressure recovery, and jet eccentricity also play a major role in determining the final hemodynamic state of AS. Moreover, mismatch between Δ P mean and AVA as in low flow low gradient AS and discordance between catheterization and echocardiographic studies in grading severity of AS have increased the complexity of AS diagnosis. The present case‐based treatise emphasizes a multi‐modality approach to delineation of the hemodynamic pathophysiology of different AS states. Key points Reduction in the aortic valve area, flow across the aortic valve, and direction of the aortic stenosis jet determine the pressure gradient generated across the aortic valve in patients with aortic stenosis. Discordance between echo and catheterization maximum gradients is related to the inherent temporal differences between the times of their acquisition. Discordance between echo and catheterization mean gradients is related to pressure recovery and assumptions in the application of Bernoulli equation to estimate the aortic valve gradient. Pressure recovery relates to the ratio of the aortic valve area and ascending aortic diameter as well as the jet direction. Mismatch between area and gradient criteria for aortic stenosis severity may occur with or without concordance between echocardiographic and catheterization data. Errors of measurement should be excluded prior to assuming any mismatch or discordance between the data. Area gradient mismatch occurs when the aortic valve area is in the severe range, while the gradient is in the non‐severe range as in low flow low gradient aortic stenosis. Reverse area gradient mismatch occurs when the gradient is in the severe range, while the aortic valve area is in the non‐severe range as in congenital aortic stenosis with an eccentric jet.
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How this classification was reachedexpand
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 | 0.003 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".