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Record W4221112548 · doi:10.1093/ehjci/jeab253

Multi-modality imaging assessment of native valvular regurgitation: an EACVI and ESC council of valvular heart disease position paper

2021· article· en· W4221112548 on OpenAlexaff
Patrizio Lancellotti, Philippe Pîbarot, John B. Chambers, Giovanni La Canna, Mauro Pepi, Raluca Dulgheru, Marc R. Dweck, Madalina Garbi, Mani A. Vannan, David Montaigne, Luigi P. Badano, Pál Maurovich‐Horvat, Gianluca Pontone, Alec Vahanian, Erwan Donal, Bernard Cosyns

Bibliographic record

VenueEuropean Heart Journal - Cardiovascular Imaging · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsValvular regurgitationRegurgitation (circulation)valvular heart diseaseMedicineCardiologyInternal medicineRadiologyModality (human–computer interaction)Computer science

Abstract

fetched live from OpenAlex

Valvular regurgitation represents an important cause of cardiovascular morbidity and mortality. Imaging is pivotal in the evaluation of native valve regurgitation and echocardiography is the primary imaging modality for this purpose. The imaging assessment of valvular regurgitation should integrate quantification of the regurgitation, assessment of the valve anatomy and function, and the consequences of valvular disease on cardiac chambers. In clinical practice, the management of patients with valvular regurgitation largely relies on the results of imaging. It is crucial to provide standards that aim at establishing a baseline list of measurements to be performed when assessing native valve regurgitation. The present document aims to present clinical guidance for the multi-modality imaging assessment of native valvular regurgitation.

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)0.011
metaresearch head score (Gemma)0.010
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.001
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.005

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 head0.039
GPT teacher head0.350
Teacher spread0.311 · 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
GenreOther

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

Citations432
Published2021
Admission routes1
Has abstractyes

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