Redo-TAVR: Essential Concepts, Updated Data and Current Gaps in Evidence
Bibliographic record
Abstract
Within the last two decades, transcatheter aortic valve replacement (TAVR) has transformed the treatment strategy for symptomatic severe aortic stenosis (AS), representing a less invasive alternative to traditional open-chest surgery. With time, advances in device features, imaging planning, and implantation techniques have contributed to an improvement in safety as well as a reduction in procedural complications. This has led to the expansion of TAVR to lower-risk patients, where TAVR has shown favorable outcomes compared to surgical aortic valve replacement (SAVR). As TAVR expands to younger and lower-risk patients with longer life expectancies, the need for reintervention for failing transcatheter heart valves is expected to increase. Redo-TAVR has gained increasing relevance in the lifetime management of AS as one of the treatment strategies available for structural valve dysfunction (SVD). However, some issues are associated with this approach, including coronary re-access and the risk of coronary obstruction. In this review, we provide essential concepts to properly select candidates for Redo-TAVR, updated data on clinical outcomes and complication rates, and current gaps in evidence.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".