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Record W2789181158 · doi:10.4244/eijv13i14a261

Concomitant mitral regurgitation: an insidious cause of lowflow, low-gradient severe aortic stenosis

2018· letter· en· W2789181158 on OpenAlexaff
Géraldine Ong, Marie‐Annick Clavel, Philippe Pîbarot

Bibliographic record

VenueEuroIntervention · 2018
Typeletter
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité LavalInstitut Universitaire de Cardiologie et de Pneumologie de Québec
Fundersnot available
KeywordsMedicineCardiologyConcomitantStenosisInternal medicineMitral regurgitationRegurgitation (circulation)

Abstract

fetched live from OpenAlex

The combination of aortic stenosis (AS) and mitral regurgitation (MR) is frequent in patients with valvular heart disease. This entity raises important challenges with regard to severity grading and therapeutic management of both AS and MR. Long-standing afterload excess associated with severe AS induces hypertrophic remodelling, dilatation and/or dysfunction of the left ventricle (LV). Secondary MR may develop in this context as a result of mitral valve leaflet tethering and mitral annular dilatation. Because of the high prevalence of concomitant coronary artery disease, ischaemic MR is also frequent in the elderly population with AS. Elderly patients may also present primary MR as a result of degenerative and calcified mitral valves. In the Euro Heart Survey, multiple valve disease, as defined by at least two moderate valvular diseases, was observed in 20% of the patients with native valve disease and in 17% of those undergoing intervention. In a Swedish nationwide study, multiple and mixed valve diseases accounted for 11% of patients and the most frequent combinations were i) AS and MR, and ii) aortic regurgitation and MR. Despite the relatively high prevalence of the concomitance of AS and MR, there are limited data on the pathophysiology, diagnosis, and management of this multiple valve disease entity.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
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 head0.029
GPT teacher head0.331
Teacher spread0.303 · 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 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

Citations3
Published2018
Admission routes1
Has abstractyes

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