Assessment of Aortic Valve Stenosis Severity
Bibliographic record
Abstract
BACKGROUND: Fluid energy loss across stenotic aortic valves is influenced by factors other than the valve effective orifice area (EOA). We propose a new index that will provide a more accurate estimate of this energy loss. METHODS AND RESULTS: An experimental model was designed to measure EOA and energy loss in 2 fixed stenoses and 7 bioprosthetic valves for different flow rates and 2 different aortic sizes (25 and 38 mm). The results showed that the relationship between EOA and energy loss is influenced by both flow rate and aortic cross-sectional area (A(A)) and that the energy loss is systematically higher (15+/-2%) in the large aorta. The coefficient (EOAxA(A))/(A(A)-EOA) accurately predicted the energy loss in all situations (r(2)=0.98). This coefficient is more closely related to the increase in left ventricular workload than EOA. To account for varying flow rates, the coefficient was indexed for body surface area in a retrospective study of 138 patients with moderate or severe aortic stenosis. The energy loss index measured by Doppler echocardiography was superior to the EOA in predicting the end points, which were defined as death or aortic valve replacement. An energy loss index </=0.52 cm(2)/m(2) was the best predictor of adverse outcomes (positive predictive value of 67%). CONCLUSIONS: This new energy loss index has the potential to reflect the severity of aortic stenosis better than EOA. Further prospective studies are necessary to establish the relevance of this index in terms of clinical outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".