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Record W2911480284 · doi:10.1097/hco.0000000000000607

Transcatheter tricuspid valve intervention

2019· review· en· W2911480284 on OpenAlexaff
Edwin Ho, Géraldine Ong, Neil Fam

Bibliographic record

VenueCurrent Opinion in Cardiology · 2019
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineMitraClipTricuspid valveRegurgitation (circulation)GuidelineIntervention (counseling)CardiologySurgeryInternal medicineIntensive care medicineMitral regurgitation

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Worldwide experience in transcatheter tricuspid valve intervention is increasing as more options become available for the treatment of severe tricuspid regurgitation. These devices can be categorized by their primary mechanism of action, including edge-to-edge leaflet devices, space occupying devices, annuloplasty devices, complete valve replacement and caval valve implantation. This review summarizes the current technologies in use, early clinical results and factors that may affect procedural success. RECENT FINDINGS: Almost all transcatheter devices for tricuspid regurgitation are investigational with very limited evidence. The most commonly used device is the MitraClip (Abbott, Santa Clara, CA, USA) edge-to-edge leaflet device, which is often more effective when the leaflet coaptation gap is not too large (ideally under 7 mm). The Tricuspid Cardioband (Edwards Lifesciences, Irvine, CA, USA) annuloplasty device has CE mark approval with promising short-term procedural results. Guideline-based assessment of disease severity and medication optimization is crucial during heart team evaluation of eligibility for intervention. SUMMARY: Although important lessons have been learned thus far regarding patient and device selection for transcatheter tricuspid regurgitation interventions, the field remains young and further research is needed to optimize treatment in terms of who, when and with what device. Our proposed algorithm for patient selection based on current knowledge incorporates both clinical and anatomic factors.

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 categoriesMeta-epidemiology (narrow), Meta-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 score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.017
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)0.0000.001

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 head0.150
GPT teacher head0.490
Teacher spread0.340 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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