Effect of concomitant asymmetric septal hypertrophy when assessing the severity of aortic valve stenosis: an in-vitro study.
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE STUDY: Aortic valve stenosis (AS) is an important cardiovascular disease that affects between 2% and 7% of the elderly population in industrialized countries. AS often coexists with asymmetric septal hypertrophy (ASH), which is generally caused by a protrusion of the hypertrophied left ventricular outflow tract (LVOT) just below the aortic valve. The study aim was to determine, based on measurement of the aortic valve effective orifice area (EOA), if ASH might potentially interfere with the assessment of AS severity. METHODS: The effects of different levels of ASH (from normal to 90%) on the EOA measured from orifices mimicking different AS severities, and from a home-built AS model constructed from a bioprosthetic aortic valve, were examined in a pulsatile flow in-vitro model. RESULTS: For the most severe AS, the level of ASH had no impact on the measured EOA. In contrast, for the less severe AS, beyond an ASH level of 50% the AS severity was progressively overestimated, and reached a reduction of about 60% of EOA for a ASH level of 90%. CONCLUSION: The presence of concomitant ASH may cause an overestimation of the hemodynamic severity of AS. The extent of overestimation is more important in less-severe AS. Hence, the presence of ASH may lead the clinician to conclude, erroneously, that the AS is severe and that aortic valve replacement is indicated. However, beyond an ASH level of 50% the AS severity can be accurately determined.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".