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Record W2115263501 · doi:10.1093/eurheartj/ehv123

The future of transcatheter mitral valve interventions: competitive or complementary role of repair vs. replacement?

2015· review· en· W2115263501 on OpenAlexaff
Francesco Maisano, Ottavio Alfieri, Shmuel Banai, Maurice Buchbinder, Antonio Colombo, Volkmar Falk, Ted Feldman, Olaf Franzen, Howard C. Herrmann, Saibal Kar, Karl-Heinz Kuck, Georg Lutter, Michael J. Mack, Georg Nickenig, Nicolò Piazza, Mark Reisman, Carlos E. Ruiz, Joachim Schöfer, Lars Søndergaard, Gregg W. Stone, Maurizio Taramasso, Alec Vahanian, John G. Webb, Stephan Windecker, Martin B. Leon

Bibliographic record

VenueEuropean Heart Journal · 2015
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsSt. Paul's HospitalUniversity of British ColumbiaMcGill University Health Centre
Fundersnot available
KeywordsMedicineMitraClipValve replacementPsychological interventionIntensive care medicineMitral valveMitral valve replacementIntervention (counseling)Mitral valve repairCardiologySurgeryStenosis

Abstract

fetched live from OpenAlex

Transcatheter mitral interventions has been developed to address an unmet clinical need and may be an alternative therapeutic option to surgery with the intent to provide symptomatic and prognostic benefit. Beyond MitraClip therapy, alternative repair technologies are being developed to expand the transcatheter intervention armamentarium. Recently, the feasibility of transcatheter mitral valve implantation in native non-calcified valves has been reported in very high-risk patients. Acknowledging the lack of scientific evidence to date, it is difficult to predict what the ultimate future role of transcatheter mitral valve interventions will be. The purpose of the present report is to review the current state-of-the-art of mitral valve intervention, and to identify the potential future scenarios, which might benefit most from the transcatheter repair and replacement devices under development.

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 (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.870
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.023
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 head0.079
GPT teacher head0.422
Teacher spread0.343 · 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 designNot applicable
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

Citations177
Published2015
Admission routes1
Has abstractyes

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