Bicuspid aortic valve associated aortopathy: 2022 guideline update
Bibliographic record
Abstract
PURPOSE OF REVIEW: Bicuspid aortic valve (BAV) disease is observed in 1-2% of the general population. In addition to valve-related complications (such as aortic stenosis and aortic regurgitation), individuals with BAV often develop dilatation of the proximal aorta (aortic root and ascending aorta), a condition termed BAV aortopathy. The development of BAV aortopathy can occur independent of valvular alterations and can lead to aneurysm formation, aortic dissection or aortic rupture. This review aims to update the clinician with an approach to BAV aortopathy decision making in keeping with the 2022 American College of Cardiology (ACC)/American Heart Association (AHA) Guideline recommendations. RECENT FINDINGS: The ACC/AHA 2022 guidelines provide a contemporary and comprehensive approach to the diagnosis and treatment of aortic pathologies. We review the thresholds for replacement of the aortic root and/or ascending aorta along with the strength and level of evidence recommendations. We also review the various Class 2A and 2B recommendations for earlier intervention, which emphasize the importance of experienced surgeons, and multidisciplinary aortic teams (MATs). SUMMARY: BAV aortopathy is a common and heterogenous clinical problem. The decision making around timing of intervention requires a personalized approach that is based on the aortic dimensions, valve function, rate of growth, family history, patient factors, and surgical experience within MATs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".