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Record W3092148399 · doi:10.1136/heartjnl-2020-317340

Measuring progression of aortic stenosis: computed tomography versus echocardiography

2020· letter· en· W3092148399 on OpenAlexaff
Ezéquiel Guzzetti, Marie‐Annick Clavel

Bibliographic record

VenueHeart · 2020
Typeletter
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
Fundersnot available
KeywordsMedicineCardiologyStenosisBicuspid aortic valveInternal medicineAortic valveCalcificationAortic valve stenosisRadiology

Abstract

fetched live from OpenAlex

Calcific aortic stenosis (AS) is characterised by an initial inflammatory phase, with endothelial damage, lipoprotein infiltration and oxidative stress. This stage is followed by a disorganisation of the extracellular matrix with an overproduction of collagen fibres and a mineralisation phase in which valvular interstitial cells differentiate into an osteoblast-like cell type, leading to microcalcification analogue to skeletal bone formation. This self-perpetuating cycle of progressive fibrocalcific remodelling leads to macrocalcification, increases valve rigidity and therefore narrows the effective orifice area, increasing left ventricular afterload and ultimately provoking myocardial damage and symptoms (figure 1).1 Valvular fibrosis, although understudied, appears to play a major role in younger, bicuspid valve and female patients and is one of the reasons sex-specific thresholds are used to define severe AS by CT aortic valve calcification (CT-AVC) quantification (2000 Agatston units (AU) for men and 1200 AU for women).2 The pathogenic model depicted in figure 1 is not necessarily followed in a serial fashion, and although the advance of the disease is inexorable, the rate of AS progression remains largely unpredictable. However, some markers of rapid progression have been described, such as systolic hypertension,3 bicuspid morphology4 and metabolic syndrome.5 Figure 1 Model of AS progression. Pathophysiological model of serial AS progression (‘aortic stenosis cascade’, in blue), along with imaging biomarkers targeting each phase (red) and potential disease-modifying treatments being currently tested in randomised clinical trials (green). 1South Korean PCSK9 inhibitors (NCT03051360); 2EAVaLL: early aortic valve lipoprotein(a) lowering (NCT02109614); 3SALTIRE II: study investigating the effect of drugs used to treat osteoporosis on the progression of calcific aortic stenosis (NCT02132026); 4BASIK2: bicuspid aortic valve stenosis and the effect of vitamin K2 on calcium metabolism on 18F-NaF PET/MRI (NCT02917525); 5EvoLVeD: early valve replacement guided by biomarkers of LV decompensation …

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: none
Teacher disagreement score0.512
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.323
Teacher spread0.282 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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