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Record W4382811735 · doi:10.17925/hi.2023.17.1.8

Low-flow, Low-gradient Severe Aortic Stenosis: A Review

2023· review· en· W4382811735 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHeart International · 2023
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsMedicineStenosisCardiologyEpidemiologyAortic valve stenosisAortic valveInternal medicinePopulationIntensive care medicineRadiologyEnvironmental health

Abstract

fetched live from OpenAlex

Aortic stenosis (AS) is a common valve pathology experienced by patients worldwide. There are limited population-based studies assessing its prevalence; however, epidemiological studies emphasize that the burden of disease is growing. Recognizing AS relies on accurate clinical assessment and diagnostic investigations. Patients who develop severe AS are often referred to the heart team for assessment of aortic valve intervention. Although echocardiography has traditionally been used to screen and monitor the progression of AS, there can be discordance between measurements in a low-flow state. Such patients may have truly severe AS and potentially derive long-term benefit from aortic valve intervention. Accurately identifying these patients with the use of ancillary testing has been the focus of research for several years. In this article, we discuss the contemporary approaches and challenges in identifying and managing patients with low-flow, low-gradient severe AS.

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.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.710
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.004
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)0.0010.005

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 head0.053
GPT teacher head0.421
Teacher spread0.369 · 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