Evaluating the AltaValve as a novel method for transcatheter mitral valve replacement
Bibliographic record
Abstract
Mitral regurgitation (MR) is the most common heart valve disease, and severe MR is associated with a poor prognosis if left untreated. Although surgical repair or replacement constitutes the standard therapy when indicated, many high-risk patients are considered ineligible for surgery. Transcatheter mitral valve replacement (TMVR) offers a less invasive alternative to conventional surgery and may also overcome some of the limitations of percutaneous repair techniques. Currently, multiple TMVR devices are undergoing clinical evaluation, showing promising results. However, challenges mainly related to the complex mitral valve anatomy along with the interaction with the left ventricular outflow tract (LVOT) have resulted in high screen failure rates among TMVR candidates. The AltaValve System features a supra-annular design, ensuring secure fixation in the left atrium above the native mitral valve annulus without anchoring mechanisms that could interfere with the left ventricle (LV). These distinctive attributes aim to address the existing TMVR limitations across a broad patient population and help to avoid complications such as LVOT obstruction, LV damage, and/or prosthesis embolization. Initial safety and feasibility data are encouraging, but a larger cohort of patients with longer follow-up will be essential to confirm the safety and efficacy of the AltaValve system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".