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Record W2757438483 · doi:10.4244/eij-d-17-00516

Imaging for structural heart procedures: focus on computed tomography

2017· review· en· W2757438483 on OpenAlexaff
John Mooney, Stephanie Sellers, Mickaël Ohana, João L. Cavalcante, Chesnal Arepalli, Rominder Grover, Ung Kim, Kapilan Selvakumar, Philipp Blanke, Jonathon Leipsic

Bibliographic record

VenueEuroIntervention · 2017
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineValve replacementContext (archaeology)Computed tomographyRadiologyModalitiesIntracardiac injectionCardiac imagingHeart valveMedical physicsCardiology

Abstract

fetched live from OpenAlex

The success and continued rapid clinical integration of transcatheter valve technologies relies on imaging modalities to guide safe and effective device deployment. In particular, cardiac imaging, using both echocardiography and CT, is an integral resource for the multidisciplinary team. These modalities can provide valuable insight for the proceduralist at each stage of transcatheter-based valve insertion, as they can be used reliably to define the anatomy of interest and its relationship to surrounding structures, determine accurate device sizing, assess patients for valve-in-valve procedures, and screen for adverse features or procedural contraindications. We provide an overview of some of the key aspects of the use of CT and echocardiography in the context of transcatheter aortic valve replacement (TAVR), as well as transcatheter mitral valve replacement (TMVR).

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.002

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 head0.071
GPT teacher head0.449
Teacher spread0.378 · 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 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

Citations11
Published2017
Admission routes1
Has abstractyes

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