Transcatheter tricuspid valve replacement: will it prevail?
Bibliographic record
Abstract
Severe tricuspid regurgitation (TR) is a prevalent and challenging condition associated with poor survival outcomes and significant morbidity. Medical therapy alone often fails to provide adequate symptom relief, and stand-alone surgical intervention is linked to high mortality rates, making it a less favorable option unless combined with left-sided valve surgery. The advent of transcatheter tricuspid interventions has provided new therapeutic possibilities, particularly for high-risk patients who are ineligible for conventional surgery. However, many patients are still unsuited for transcatheter tricuspid repair or have only limited benefits from such procedures. In this context, Transcatheter tricuspid valve replacement (TTVR) has rapidly emerged as a promising alternative, offering the potential for more effective treatment outcomes. This review explores the latest advancements in TTVR devices, highlights key clinical findings, and discusses the challenges and limitations of this evolving strategy. Additionally, we address patient selection criteria, procedural outcomes, and future directions in the field, emphasizing the potential of TTVR to transform the management of severe TR.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".