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Record W4399341905 · doi:10.1097/ms9.0000000000002106

Insights into calcific aortic valve stenosis: a comprehensive overview of the disease and advancing treatment strategies

2024· article· en· W4399341905 on OpenAlexaff
Hritvik Jain, Aman Goyal, Abeer T.M.A. Khan, Noor Ullah Khan, Jyoti Jain, Shrey Chopra, Samia Aziz Sulaiman, G. M. Reddy, Kush Patel, Kaarvi Khullar, Mohamed Daoud, Amir Humza Sohail

Bibliographic record

VenueAnnals of Medicine and Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsMedicineCalcificationStenosisCardiologyAortic valveDiseaseModalitiesRadiologyAortic valve stenosisCalcinosisInternal medicineHeart valveTreatment modality

Abstract

fetched live from OpenAlex

Aortic valve stenosis is a disease characterized by thickening and narrowing of the aortic valve (AV), most commonly due to calcification, which leads to left ventricular outflow obstruction called calcific aortic valve disease (CAVD). CAVD presents as a progressive clinical syndrome with cardiorespiratory symptoms, often with rapid deterioration. The modern-day pathophysiology of CAVD involves a complex interplay of genetic factors, chronic inflammation, lipid deposition, and valve calcification, with early CAVD stages resembling atherosclerosis. Various imaging modalities have been used to evaluate CAVD, with a recent trend of using advanced imaging to measure numerous AV parameters, such as peak jet velocity. Significant improvements in mortality have been achieved with transcatheter AV repair, but numerous therapeutics and modalities are being researched to delay the progression of CAVD. This article aims to provide a comprehensive review of CAVD, explore recent developments, and provide insights into future treatments with various novel modalities.

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 score0.224
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.097
GPT teacher head0.403
Teacher spread0.306 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

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