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Record W4234844359 · doi:10.1093/eurheartj/ehr259

EAE/ASE recommendations for the use of echocardiography in new transcatheter interventions for valvular heart disease

2011· article· en· W4234844359 on OpenAlexaff
José Luis Zamorano, Luigi P. Badano, Charles J. Bruce, Kah Lok Chan, Alexandra Gonçalves, Rebecca T. Hahn, Martin Keane, Giovanni La Canna, Mark Monaghan, Petros Nihoyannopoulos, Frank E. Silvestry, Jean‐Louis Vanoverschelde, Linda D. Gillam, Alec Vahanian, Vitantonio Di Bello, T. Buck

Bibliographic record

VenueEuropean Heart Journal · 2011
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicinevalvular heart diseaseCardiologyInternal medicineCatheterPsychological interventionMitral valveHeart diseaseIntensive care medicineRadiology

Abstract

fetched live from OpenAlex

The introduction of devices for transcatheter aortic valve implantation, mitral repair, and closure of prosthetic paravalvular leaks has led to a greatly expanded armamentarium of catheter-based approaches to patients with regurgitant as well as stenotic valvular disease. Echocardiography plays an essential role in identifying patients suitable for these interventions and in providing intra-procedural monitoring. Moreover, echocardiography is the primary modality for post-procedure follow-up. The echocardiographic assessment of patients undergoing transcatheter interventions places demands on echocardiographers that differ from those of the routine evaluation of patients with native or prosthetic valvular disease. Consequently, the European Association of Echocardiography in partnership with the American Society of Echocardiography has developed the recommendations for the use of echocardiography in new transcatheter interventions for valvular heart disease. It is intended that this document will serve as a reference for echocardiographers participating in any or all stages of new transcatheter treatments for patients with valvular heart disease.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.005
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 head0.259
GPT teacher head0.406
Teacher spread0.147 · 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.

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

Citations320
Published2011
Admission routes1
Has abstractyes

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