The future of sutureless valve technology
Bibliographic record
Abstract
Abstract: The use of sutureless valves (SV) has been emerging as a promising surgical option in certain patient populations due to their straightforward and fast deployment technique and the encouraging mid- and long-term outcomes compared to surgical aortic valve replacement (SAVR) with stented valves. Main concerns associated with the use of this technology include increased rates of paravalvular leak (PVL) and a higher need for permanent pacemaker implantation (PPI); however, the reported outcomes have been improving over the years. This may be partly due to more surgeon exposure and experience in using these valves, a better understanding of the risk factors, and the improvement in the sizing techniques of these valves. The recently released new generation PercevalTM PLUS valve also offers an adapted design that reduces the protrusion of the valve into the left ventricular outflow tract (LVOT), which may further decrease the risk of damage to the conduction system. Recent studies have also shown supporting evidence that the use of SV may have potential in some complex situations, such as in patients with bicuspid aortic valves (BAV) and/or patients with dilated aortic annuli; however, these applications are yet to be endorsed by data derived from large-scale clinical studies. Future research focused on improving tissue durability and developing valve design may be key in the expansion of the use of this technology.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".