A Contemporary Look at the Landscape of Treatment of Tricuspid Regurgitation
Bibliographic record
Abstract
Importance: Untreated severe tricuspid regurgitation carries a poor prognosis. We aim to provide a contemporary review of the anatomy, clinical manifestations, and diagnostic and management strategies, including medical, surgical and transcatheter options. By synthesizing current knowledge, this review seeks to equip clinicians with the insights necessary to navigate the complexities of TR treatment. Observations: Tricuspid regurgitation is predominantly secondary to annular dilation and leaflet tethering but can also be associated with cardiac implantable electronic device leads and primary leaflet pathologies. Isolated tricuspid valve surgery is infrequently performed, especially in high surgical risk patients, prompting the emergence of transcatheter treatment options. These advancements are complemented by significant strides in multimodality imaging, including three-dimensional echocardiography, computed tomography, and magnetic resonance imaging, which enhance diagnostic accuracy and procedural planning. Conclusions and Relevance: The effective management of tricuspid regurgitation necessitates a multidisciplinary approach, integrating input from interventional cardiology, cardiac surgery, heart failure cardiology, imaging, and electrophysiology. Surgical and transcatheter interventions such as tricuspid transcatheter-edge-to-edge repair and transcatheter tricuspid valve replacement have demonstrated favorable early clinical and functional outcomes, but ongoing research is necessary to refine patient selection and improve treatment decision-making. Individualizing treatment plans to optimize health outcomes and quality of life for patients with tricuspid regurgitation is paramount.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| 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".