Utilizing wall shear stress as a clinical biomarker for bicuspid valve-associated aortopathy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Bicuspid aortic valve (BAV) results from fusion of two adjacent aortic valve cusps, and is associated with dilatation of the aorta, known as BAV-associated aortopathy, or bicuspid aortopathy. Bicuspid aortopathy is progressive, increasing the risk of life-threatening clinical events, such as aortic dissection. Regular monitoring and timely intervention with prophylactic surgical resection of the proximal aorta is recommended. RECENT FINDINGS: Aortopathy is heterogeneous among patients. Studies have shown that different flow patterns lead to specific phenotypes of aortopathy. Although not uniform, BAV morphology affects flow patterns. Recent work has demonstrated the role of wall shear stress (WSS) in driving aortopathy, and it is suggested that individualized WSS 'heat maps' can be used for clinically monitoring patients with BAV. WSS has the potential to be an imaging biomarker for directing resection timing, surgical strategies, and postsurgical follow-up care. SUMMARY: Finding and validating noninvasive hemodynamic biomarkers of aortic risk to assist in the management of BAV patients is of clinical importance. Herein, we will review the latest findings pertaining to the utility of WSS as a specific biomarker of risk for BAV patients with aortopathy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".