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Record W3116028803 · doi:10.3389/fcvm.2020.592909

Current Devices in TMVI and Their Limitations: Focus on Tendyne

2020· article· en· W3116028803 on OpenAlexaboutno aff
Gry Dahle

Bibliographic record

VenueFrontiers in Cardiovascular Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMitral regurgitationCardiologyMitraClipCatheterMitral valveInternal medicineImplantRegurgitation (circulation)Surgery

Abstract

fetched live from OpenAlex

Mitral valve regurgitation (MR) has a high incidence in the western world, and mortality is high for untreated severe MR. Catheter based repair was introduced with MitraClip in 2003, and some additional devices later came into the market. To expand the transcatheter treatment options for mitral valve disease, the first transcatheter mitral valve implantation (TMVI) was performed by Søndergaard et al. 2012, only 10 years after the first transcatheter aortic valve implantation (TAVI), however, the development has been much slower for the TMVI than for TAVI. From 2012, studies were started for several devices to prove feasibility and safety. However, there were big challenges in valve design; delivery systems and anchoring in addition to anatomical issues (avoid LVOT obstruction and paravalvular leak, big size of annulus). The main valves in studies were CardiaQ (later bought by Edwards Lifesciences, Irvine, United States), Tiara (Neovasc Inc., Richmond, Canada), Twelve (later Intrepid, Medtronic, MN, United States) and Tendyne™ (Abbott, MN, United States). I will focus on the Tendyne™ valve that is the only CE approved transcatheter mitral valve implant. It is available in a large number of sizes and is repositionable and retrievable. The results for the 100 first patients included in the early feasibility study (EFS) at 1 and 2 years are promising. Initially feasible for MR, but further investigations show promising results also for implant in mitral annular calcification.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.799
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.293
Teacher spread0.262 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations27
Published2020
Admission routes1
Has abstractyes

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