Speckle tracking echocardiography: imaging insights into the aorta
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pathophysiologic changes of aortic tissue may not always manifest as aneurysms, nor does the size of an aneurysm necessarily represent the severity of tissue abnormality - approximately 40% of patients who present with dissection have aortic diameters below criteria recommended for surgical resection. Noninvasive imaging-based quantification of aortic biomechanics has the potential to improve our knowledge of the pathophysiology of aortic disease, including patient-specific risk-stratification and intraoperative surgical decision-making. RECENT FINDINGS: We summarize the current state of clinical utilization of two-dimensional speckle tracking echocardiography (2D-STE) aortic strain to better understand the pathophysiology, clinical implications, and risk stratification of aortic disease. SUMMARY: 2D-STE has demonstrated promising early results as an imaging modality to determine clinically relevant measures of aortic tissue mechanical properties. Further large multinational, multiethnic, age-stratified, and sex-stratified measures of normal aortic strain measurements, as well as comparison studies with alternative imaging techniques, will be needed to properly elucidate the role echocardiography will play in the clinical management of aortic disease.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".