Transcatheter aortic valve-in-surgical aortic valve implantation: current status and future perspectives
Bibliographic record
Abstract
During the last decade, the relative use of surgical bioprosthetic valves has increased by nearly 80%, an observation likely explained by improved surgical techniques, valve durability, avoidance of anticoagulation and patient’s preference [1]. Nevertheless, surgical bioprosthetic valves are known to fail; actuarial freedom from reoperation for a failing bioprosthetic valve is 95, 90 and 70% at 5, 10 and 15 years, respectively [2, 3]. The lifetime risk of reoperation actually decreases with increasing patient age at the time of the index procedure. For example, a 50and a 60-year old patient undergoing surgical bioprosthetic aortic valve replacement will have a lifetime risk of reoperation of 45 and 25%, respectively [4]. The gold-standard treatment for a failing surgical bioprosthetic valve is a redo operation. The operative mortality following an elective redo operation in low-to-intermediate risk patients is 2–7% [5, 6]. In high surgical risk or non-elective cases, however, the mortality can be as high as 30% [7]. Even in low-risk and elective redo scenarios, the risks of wound infection, blood transfusions, postoperative pain and delayed functional recovery are not negligible. Transcatheter aortic valve-in-surgical aortic valve (TAV-in-SAV) implantation was first reported in 2007, and since then, numerous case series and registries have demonstrated its safety and efficacy [8, 9]. Since TAV-in-SAV avoids sternotomy and cardiopulmonary bypass, it can improve resource utilization by accelerating patient recovery and by reducing the length of hospital stay. Furthermore, it may obviate or reduce the number of repeat surgical procedures in a patient’s lifetime.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".