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Abstract 13173: Impact of Bicuspid Aortic Valve Morphology on Computed Tomography Valvular Calcification in Aortic Stenosis and Sex Differences

2022· article· en· W4380795097 on OpenAlexaff
Zi Ye, Marie‐Annick Clavel, Thomas A. Foley, Philippe Pîbarot, Maurice Enriquez‐Sarano, Héctor I. Michelena

Bibliographic record

VenueCirculation · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecMontreal Heart Institute
Fundersnot available
KeywordsMedicineBicuspid aortic valveCardiologyInternal medicineAortic valveStenosisCalcificationRegurgitation (circulation)Bicuspid valveAortic valve stenosis

Abstract

fetched live from OpenAlex

Introduction: Aortic valve calcification (AVC) is the key pathophysiology underlying aortic stenosis (AS). We aimed to assess computed tomography AVC in AS by valve morphology (bicuspid [BAV] vs. tricuspid aortic valve[TAV]) and sex. Methods: Retrospective study of patients with echocardiographic AS severity and AVC assessments within six months. Patients with > moderate aortic regurgitation, prosthetic aortic valve, or indeterminate aortic valve morphology were excluded. Severe AS with high mean gradient (MG) was defined as aortic valve area (AVA)≤1cm2 or indexed AVA≤0.6 cm2/m2 + MG≥40 mmHg. Results: Of 2647 patients, 361(65% men) had BAV and 2286(59% men) had TAV, age 65±11 vs. 80±9 years(p<0.001), AVA 1.07±0.42 vs. 0.90±0.25 cm2(p<0.001), MG 41±16 vs.42±14 mmHg(p=0.4) for BAV and TAV, respectively. For both BAV and TAV groups, age, MG, and severe AS frequency were similar between women and men(all p≥0.06). BAV morphology affected AVC-AS severity and AVC-sex associations (p for interactions <0.001): as AS severity increased, mean AVC difference became larger between BAV-men and TAV-men, but smaller between BAV-women and TAV-women. Similar patterns were observed in AVC density (AVCd, AVC divided by left ventricular outflow tract diameter)-AS severity and AVCd-sex associations (p for interactions < 0.001). In patients with concordant AS severity and MG levels, the best BAV AVC-cutoff for detecting severe AS + high MG by area-under-the-curve analysis was 924 AU for BAV-women and 2851 AU for BAV-men, different from guideline-recommended AVC-thresholds (1200 for women and 2000 for men). The best BAV AVCd cutoff was 289 for BAV-women and 476 for BAV-men, similar to the cutoffs for patients predominantly with TAV (292 for women and 476 for men from Clavel MA JACC 2013). The BAV-AVC threshold identified more patients with severe AS than guideline-recommended threshold in BAV-women (84% vs 74%, p<0.001), but not in BAV-men. Conclusions: Valvular calcium accretion in AS was different by valve morphology and sex. As compared to TAV, BAV-women exhibited the least calcification while BAV-men exhibited the highest. Such patterns may impact the sensitivity of guideline-recommended AVC thresholds for detecting severe AS in BAV patients.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.024
GPT teacher head0.324
Teacher spread0.300 · 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 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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