Echocardiographic predictors of outcomes in adults with aortic stenosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: The study purpose was to assess the usefulness of echocardiographic parameters of aortic stenosis (AS) severity and left ventricular (LV) systolic function to predict mortality in AS. The main hypothesis is that parameters of LV systolic function are the most important independent predictors of mortality, whereas parameters of stenosis severity are not. METHODS: 1065 consecutive patients with AS referred to the echocardiography laboratory and meeting the inclusion/exclusion criteria were included and followed during 5.7 years. The end points were aortic valve replacement (AVR) (n=584), composite of AVR or death (n=932), all-cause mortality (n=550) and cardiovascular mortality (n=398). RESULTS: The most powerful echocardiographic predictors of valve-related events were parameters of AS severity, such as peak aortic jet velocity (VPeak), mean gradient (MG) and aortic valve area (AVA) (all p<0.001). Regarding mortality, the main predictors were LV ejection fraction (LVEF) and stroke volume index (SVi) (p<0.05). After multivariable adjustment, LVEF (p<0.001) and SVi (p=0.02) remained the only echocardiographic predictors of mortality, even after adjustment for symptomatic status. AVA was also associated with mortality, whereas VPeak and MG were not. CONCLUSIONS: The most powerful echocardiographic predictors of mortality are low LVEF and low flow, whereas AS severity parameters predict valve-related events but not overall mortality. Hence, low flow should be integrated in the risk stratification and therapeutic decision-making in patients with AS.
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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 | 0.000 |
| 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 it