Bibliographic record
Abstract
PURPOSE OF REVIEW: Tricuspid transcatheter edge-to-edge repair (T-TEER) has emerged as a well tolerated and effective therapeutic option for many patients with symptomatic severe tricuspid regurgitation at prohibitive surgical risk. However, there remain several important limitations to clip-based technology in the context of other rapidly emerging percutaneous treatment options for tricuspid regurgitation. RECENT FINDINGS: Tricuspid lesions pose unique challenges to treatment with the current toolbox of transcatheter clip-based technologies. This review will explore key issues related to patient factors, anatomical factors, and imaging factors that may render lesions to be unsuitable for treatment with T-TEER. SUMMARY: Selection for T-TEER must include a detailed clinical evaluation in the context of a 'heart team' approach involving multiple subspecialists, with screening for patient/lesion characteristics that make T-TEER suboptimal with current clip-based technologies. Future directions for research include patient-specific 3D modeling techniques, leaflet grasping techniques, clip deployment strategies, and personalized device sizing to increase the spectrum of lesions that may be treated with T-TEER within the context of other emerging transcatheter treatment options.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".