Innovations in tricuspid valve intervention
Bibliographic record
Abstract
PURPOSE OF REVIEW: Tricuspid valve disease has received much less attention in terms of intervention. The main reason for this is the widely held belief that treatment of left-sided valve disease leads to resolution of functional tricuspid regurgitation. Recent data show that tricuspid regurgitation is not benign and that many patients will benefit from intervention at the time of left-sided valve surgery, or in isolated tricuspid disease. This review describes the latest surgical and interventional options and strategies. RECENT FINDINGS: Latest valve guidelines now recommend a more aggressive surgical approach to the treatment of moderate or severe tricuspid regurgitation, with annuloplasty being the preferred technique. Guidelines now also promote treatment of isolated significant tricuspid dilatation even without significant regurgitation, as a prophylactic strategy to prevent disease progression. This renewed interest in surgical repair has been accompanied by development of newer tricuspid annuloplasty rings. For patients in whom surgery would be high risk, transcatheter therapies are emerging as a promising alternative. Various repair devices have reported early experience. SUMMARY: Recent surgical and transcatheter innovations in the treatment of tricuspid valve disease are promising and have the potential of removing the stigma of the tricuspid valve as the 'forgotten valve'.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".