Assessment of low-flow, low-gradient aortic stenosis: multimodality imaging is the key to success
Bibliographic record
Abstract
In patients with aortic stenosis (AS), a low-flow state may occur with reduced LV ejection fraction (LVEF) (i.e., classic low flow) or with preserved LVEF (i.e., paradoxical low flow) and it is often associated with low gradient because the gradient is highly flow-dependent. Low-flow, low-gradient (LF-LG) AS is a frequent clinical entity generally associated with worse outcomes. A multimodality imaging approach, including comprehensive resting echocardiography, dobutamine stress echocardiography (DSE), and multidetector computed tomography (MDCT), is the key to successful management of patients with LF-LG AS, who represent a highly challenging subset from both a diagnostic and a therapeutic standpoint. DSE and quantification of aortic valve calcification by MDCT provide important information that is crucial to differentiate true-severe from pseudo-severe AS and therefore select the most appropriate therapy (i.e., AVR vs. medical). The assessment of LV flow reserve by DSE is useful to stratify the operative risk and guide decision making between surgical and transcatheter AVR. Other imaging biomarkers, such as the global LV longitudinal strain measured during DSE or the amount of myocardial fibrosis assessed by cardiac magnetic resonance imaging, may provide incremental information for risk stratification and therapeutic management in LF-LG AS, but additional studies are needed to validate and refine these emerging biomarkers further.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".