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Record W4410243211 · doi:10.1080/14796678.2025.2499352

Evaluating the AltaValve as a novel method for transcatheter mitral valve replacement

2025· review· en· W4410243211 on OpenAlexaff
Pablo Vidal-Calés, Pedro Cepas‐Guillén, Juan Portillo, Josep Rodés‐Cabau

Bibliographic record

VenueFuture Cardiology · 2025
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedicineCardiologyValve replacementInternal medicineMitral valve replacementMitral valveFunctional mitral regurgitationStenosisHeart failureEjection fraction

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.916
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.046
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.503
Teacher spread0.424 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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