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Record W2890472080 · doi:10.21037/jtd.2018.09.02

Patient screening for early detection of aortic stenosis (AS)—review of current practice and future perspectives

2018· review· en· W2890472080 on OpenAlexaff
Martin Thoenes, Peter Bramlage, Pepe Zamorano, David Messika–Zeitoun, Daniel Wendt, Markus Kasel, Jana Kurucova, Richard P. Steeds

Bibliographic record

VenueJournal of Thoracic Disease · 2018
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineLife expectancyAsymptomaticIntervention (counseling)Intensive care medicineStenosisDiseaseEpidemiologyHealth carePediatricsPopulationSurgeryInternal medicineNursing

Abstract

fetched live from OpenAlex

Abstract: In Europe, approximately one million people over 75 years suffer from severe aortic stenosis (AS), one of the most serious and most common valve diseases, and this disease burden is increasing with the aging population. A diagnosis of severe symptomatic AS is associated with an average life expectancy of 2–3 years and necessitates a timely valve intervention. Guidelines for valve replacement therapy have been established but only a proportion of patients with symptomatic AS actually receive this life-saving treatment. The decision for valve intervention in asymptomatic patients with severe AS is often more challenging and likely results in fewer patients receiving treatment in comparison to their symptomatic counterparts. This article reviews the epidemiology and clinical manifestations of AS, the associated economic burden of AS to the healthcare system, the diagnosis of AS and the possible mechanisms for the introduction of routine screening in elderly patients. Elderly patients typically visit healthcare providers more frequently than younger patients, thereby providing increased opportunities for ad hoc AS screening and this, along with raising patient awareness of the symptoms of AS, has the potential to result in the earlier diagnosis and treatment of AS and increased patient survival.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.806
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.462
Teacher spread0.421 · 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 designOther design
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

Citations59
Published2018
Admission routes1
Has abstractyes

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