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Record W2041919798 · doi:10.4244/eijv5i1a13

Percutaneous suture edge-to-edge repair of the mitral valve

2009· article· en· W2041919798 on OpenAlexaff
John G. Webb, Francesco Maisano, Alec Vahanian, Brad Munt, Tasneem Z. Naqvi, Raoul Bonan, David Zarbatany, Maurice Buchbinder

Bibliographic record

VenueEuroIntervention · 2009
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité de MontréalSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicinePercutaneousFibrous jointMitral regurgitationSurgeryMitral valve repairMitral valveDehiscenceCardiac surgeryCardiology

Abstract

fetched live from OpenAlex

AIMS: To describe a new approach to percutaneous mitral valve repair and an illustrative first-in-man experience, we introduce a suture mediated "double orifice", "edge-to-edge" procedure which can be an effective surgical therapy for mitral regurgitation (MR) in selected patient. METHODS AND RESULTS: We describe a novel percutaneous approach to double orifice mitral repair utilising an intra-cardiac suture based system. The procedure was performed in 15 patients in four international centres. Endovascular suture based double orifice mitral repair was feasible with an acute reduction in the severity of MR by > or = 1 grade in nine of 15 patients. At 30 days improvement in MR appeared durable in six patients. Clinical utility was limited by technical difficulties, the inadequacies of current imaging modalities and suture dehiscence. CONCLUSIONS: Percutaneous endovascular suture based cardiac repair is feasible. However, in utilising the current device clinical benefit was limited and the repair not durable. In the future, similar endovascular approaches may enable more complex cardiac repair.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.013
GPT teacher head0.320
Teacher spread0.307 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations39
Published2009
Admission routes1
Has abstractyes

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