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Record W3082194726 · doi:10.1080/24748706.2020.1817643

Mixed Aortic Valve Disease: A Diagnostic Challenge, a Prognostic Threat

2020· article· en· W3082194726 on OpenAlexaff
Philippe Unger, Marie‐Annick Clavel

Bibliographic record

VenueStructural Heart · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
Fundersnot available
KeywordsMedicineRegurgitation (circulation)StenosisCardiologyAsymptomaticInternal medicinevalvular heart diseaseRadiologyEjection fractionAortic valve stenosisMagnetic resonance imagingDiseaseHeart failure

Abstract

fetched live from OpenAlex

Mixed aortic valve disease, defined as the combination of aortic stenosis and regurgitation, is a frequent condition for which a poor prognosis has recently been demonstrated, likely as a result of its specific pathophysiology. Echocardiography, based on consecutive evaluation of stenosis, regurgitation, and global effects caused by the valve disease, is the diagnostic cornerstone of severity assessment, but advances in imaging, including speckle-tracking echocardiography, multidetector computed tomography, and cardiac magnetic resonance, may improve the diagnostic yield. Current surgical or transcatheter management is mainly based on criteria used for isolated stenosis or regurgitation, including symptoms and left ventricular dilatation and/or dysfunction, but emerging evidence tends to support earlier management. Ideally, a randomized trial in asymptomatic patients with preserved ejection fraction would be needed to evaluate the potential benefit of this approach.

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)0.007
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0030.006
Open science0.0010.002
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.029
GPT teacher head0.328
Teacher spread0.299 · 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 designCase report
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

Citations21
Published2020
Admission routes1
Has abstractyes

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