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Record W2556086061 · doi:10.1097/hco.0000000000000359

Bicuspid aortic valve aortopathy

2016· review· en· W2556086061 on OpenAlexaff
David G. Guzzardi, Subodh Verma, Paul W.M. Fedak

Bibliographic record

VenueCurrent Opinion in Cardiology · 2016
Typereview
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsBP (Canada)St. Michael's HospitalUniversity of CalgaryLibin Cardiovascular Institute of Alberta
Fundersnot available
KeywordsBicuspid aortic valveMedicineStenosisCardiologyDiseaseClinical PracticeInternal medicinevalvular heart diseaseClinical phenotypeAortic valve replacementIntensive care medicinePhenotypePhysical therapy

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This focused review summarizes key insights from the past 12 months of basic science and clinical research on bicuspid aortic valve (BAV)-associated aortopathy. RECENT FINDINGS: Recent studies in BAV-associated aortopathy support a heterogeneous spectrum of disease with distinct phenotypes. Basic science studies provide further support for the concept of regional differences in the severity of aortopathy within the aorta of BAV patients. Clinical studies compared outcomes of BAV patients after isolated aortic valve replacement and showed that those with primarily valvular insufficiency as compared with stenosis may be at greater risk for important aortic events over time. These novel insights will be important to optimize future aortic resection strategies and clinical practice guidelines. SUMMARY: As the most common congenital heart defect, BAV disease is a considerable health burden. Recent studies show differences in the clinical manifestation of disease patterns that may have important implications for future research and the evolution toward more patient-specific surgical practice guidelines.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.158
GPT teacher head0.429
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations20
Published2016
Admission routes1
Has abstractyes

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