Transcatheter aortic valve replacement program development: Recommendations for best practice
Bibliographic record
Abstract
BACKGROUND: Transcatheter aortic valve replacement (TAVR) is an increasingly available therapy for the management of aortic stenosis in higher risk populations. Beyond addressing the procedural challenges, centers must attend to the unique requirements of developing TAVR programs from referral to follow-up. AIM: The aim of this article is to outline the recommendations for best practice for program development from centers with early and extensive experience. RECOMMENDATIONS: The guideline-recommended Heart Team approach requires interdisciplinary agreements, delineation of roles and responsibilities, and the development of the role of the TAVR Coordinator. To support appropriate case selection, the screening and evaluation must be organized in a comprehensive clinic visit. In addition to the multimodality imaging tests, the assessment of functional status and frailty is pivotal to the eligibility decision. Throughout the TAVR trajectory, careful attention must be afforded to the integration of geriatric best practices. Pre-procedure care requires patient and family education to manage expectations and facilitate early discharge planning. Peri-procedural care planning, including equipment requirements, monitoring protocols, and emergency intervention agreements, contributes to procedural success. The aims of post-procedure care are to monitor the recovery, facilitate the rapid return to baseline status, and optimize length of stay. TAVR programs require data management strategies to facilitate and monitor program growth, support program evaluation, and meet the requirements for submission to national registries. CONCLUSION: TAVR represents a paradigm shift in the management of structural heart disease. Programmatic success and patient outcomes depend on the development of a comprehensive and collaborative program tailored to TAVR.
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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 | 0.058 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.016 | 0.011 |
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".