TAVI: from home-made prosthesis to global interventional phenomenon
Bibliographic record
Abstract
Surgical aortic valve replacement (AVR) was first developed in the 1960s. The procedure rapidly became first-line therapy for the management of symptomatic severe aortic stenosis (AS). Concurrently, percutaneous catheter-mounted valves were evaluated in animals, although these did not make it into clinical use. In the 1980s, balloon aortic valvuloplasty appeared as an alternative to AVR. However, despite short-term symptomatic relief and improved haemodynamics, valvuloplasty did not change the natural course of the disease. Collapsible metal scaffolds with biological valves for permanent implantation were investigated in animal models in the early 1990s. In 2002, the first-in-human transcatheter aortic valve implantation (TAVI) was performed via an antegrade, transvenous approach. Later, the retrograde approach, with access via the femoral artery, gained favour and became a reproducible, fully percutaneous procedure. Recently, subclavian access has also proven to be a feasible and reproducible alternative. Both the antegrade transapical and retrograde direct aortic approaches now offer alternatives for patients with unsuitable vascular access via the femoral route. Within a decade, TAVI spread throughout the world, became approved by regulatory authorities, and is now part of the armamentarium in the treatment of AS as a lifesaving, but less invasive, procedure.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".