Clinical Research on Transcatheter Aortic Valve Replacement for Bicuspid Aortic Valve Disease: Principles, Challenges, and an Agenda for the Future
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
Bicuspid aortic valve disease (BAVD) is present in up to half of all patients referred for surgical aortic valve replacement (SAVR) yet was an exclusion criterion for all randomized controlled trials (RCTs) comparing transcatheter aortic valve replacement (TAVR) to SAVR. Nonetheless, approximately 10% of patients currently treated with TAVR have BAVD and available observational data for performing TAVR in these patients are limited by selection bias. Many in the cardiovascular community have advocated for RCTs in this population, but none have been performed. The Heart Valve Collaboratory (HVC) is a multidisciplinary community of stakeholders with the aim of creating significant advances in valvular heart disease by stimulating clinical research, engaging in educational activities, and advancing regulatory science. In December 2020, the HVC hosted a Global Multidisciplinary workshop involving over 100 international experts in the field. Following this 2-day symposium, working groups with varied expertise were convened to discuss BAVD, including the need for and design of RCTs. This review, conducted under the auspices of the HVC, summarizes available data and knowledge gaps regarding procedural therapy for BAVD, outlining specific challenges for trials in this population. We also propose several potential studies that could be performed and discuss respective strengths and weaknesses of each approach. Finally, we present a roadmap for future directions in clinical research in TAVR for BAVD with an emphasis both on RCTs and also prospective registries focused on disease phenotyping to develop parameters and risk scores that could ultimately be applied to patients to inform clinical decision-making.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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