Energy Loss Index in Aortic Stenosis
Bibliographic record
Abstract
Cardiology and European Society of Cardiology-European Association for Cardio-Thoracic Surgery guidelines, 1,2 aortic valve replacement (AVR) is considered a class I indication in patients with aortic stenosis (AS) if the stenosis is severe and the patient has symptoms or left ventricular ejection fraction <50%.Hence, accurate assessment of the hemodynamic severity of the valvular stenosis is crucial for clinical decision making.The stenosis severity is generally determined by measuring the transvalvular pressure gradient or the aortic valve effective orifice area (AVA); however, these conventional parameters do not account for the extent of pressure recovery that may occur downstream of the stenosis.In an article published in 2000 in Circulation, 3 we proposed a new Doppler echocardiographic parameter based on the energy loss concept to adjust the AVA for pressure recovery, and we postulated that this energy loss index (ELI) would improve assessment of stenosis severity and risk stratification in AS.Thirteen years later, Bahlmann and colleagues 4 publish in this issue of Circulation the first prospective study to demonstrate that ELI provides independent and incremental prognostic information to that derived from conventional measures of AS severity.In this elegant substudy of the SEAS (Simvastatin Ezetimibe in Aortic Stenosis) trial, the authors report that a 1 cm²/m² reduction in baseline ELI predicts a 2-fold increase in the risk of aortic valve events and of the composite of mortality and heart failure hospitalization after adjustment for peak aortic jet velocity or mean gradient.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".