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Record W3183282238 · doi:10.1097/hco.0000000000000887

Assessment of the tricuspid valve using cardiovascular magnetic resonance

2021· review· en· W3183282238 on OpenAlexaff
Yang Zhan, Dipan J. Shah

Bibliographic record

VenueCurrent Opinion in Cardiology · 2021
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsRegina General Hospital
Fundersnot available
KeywordsMedicineTricuspid valveRegurgitation (circulation)CardiologyMagnetic resonance imagingInternal medicineHeart diseaseCardiac magnetic resonance imagingGold standard (test)Cardiac magnetic resonanceRadiology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The rapid search for suitable tricuspid transcatheter devices has ignited renewed enthusiasm in accurate characterization of tricuspid valve disease. Cardiovascular magnetic resonance (CMR), traditionally used as the gold standard in assessment for right ventricular size and function, has recently seen its use expanded to assess both the structure and function of the tricuspid apparatus. This review will highlight the role of CMR in tricuspid valve disease and compare it with other commonly used imaging modalities. RECENT FINDINGS: Dynamic anatomical assessment of the tricuspid apparatus, in combination with accurate leaflet identification, is possible with CMR. Tricuspid regurgitation volume and fraction are derived through an indirect volumetric method, and therefore, able to overcome many traditional hurdles involved with valve regurgitation quantitation. Adverse right heart prognostic factors in tricuspid valve disease, such as right heart volumes, function, and tissue characterization, are optimally assessed using CMR. SUMMARY: Cardiovascular magnetic resonance is a powerful modality that should be harnessed in order to obtain a multifaceted assessment of tricuspid valve structure, function, and the effects of valve disease on right heart remodeling.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.015
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.126
GPT teacher head0.471
Teacher spread0.345 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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