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Record W2907273540 · doi:10.1016/j.jcmg.2018.10.023

The Role of Imaging in Measuring Disease Progression and Assessing Novel Therapies in Aortic Stenosis

2019· review· en· W2907273540 on OpenAlexaff
Mhairi Doris, Russell J. Everett, Matthew Shun‐Shin, Marie‐Annick Clavel, Marc R. Dweck

Bibliographic record

VenueJACC. Cardiovascular imaging · 2019
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité Laval
FundersBritish Heart FoundationNational Institute for Health and Care Research
KeywordsMedicineStenosisDiseaseCardiologyClinical trialPathophysiologyIntensive care medicineInternal medicineValve replacementAortic valve stenosisClinical PracticePhysical therapy

Abstract

fetched live from OpenAlex

Aortic stenosis represents a growing health care burden in high-income countries. Currently, the only definitive treatment is surgical or transcatheter valve intervention at the end stages of disease. As the understanding of the underlying pathophysiology evolves, many promising therapies are being investigated. These seek to both slow disease progression in the valve and delay the transition from hypertrophy to heart failure in the myocardium, with the ultimate aim of avoiding the need for valve replacement in the elderly patients afflicted by this condition. Noninvasive imaging has played a pivotal role in enhancing our understanding of the complex pathophysiology underlying aortic stenosis, as well as disease progression in both the valve and myocardium. In this review, the authors discuss the means by which contemporary imaging may be used to assess disease progression and how these approaches may be utilized, both in clinical practice and research trials exploring the clinical efficacy of novel therapies.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.750
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.004
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 head0.030
GPT teacher head0.356
Teacher spread0.326 · 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
GenreReview

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

Citations33
Published2019
Admission routes1
Has abstractyes

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