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Record W3187723485 · doi:10.4244/eij-d-21-00363

Membranous septum morphology and risk of conduction abnormalities after transcatheter aortic valve implantation

2022· article· en· W3187723485 on OpenAlexaff

Bibliographic record

VenueEuroIntervention · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsConduction abnormalitiesMorphology (biology)Electrical conduction system of the heartSignificant differenceEnhanced Data Rates for GSM EvolutionValve replacement

Abstract

fetched live from OpenAlex

BACKGROUND: There are limited data on the association of membranous septum (MS) morphology and transcatheter heart valve (THV) implantation depth, and the development of new conduction abnormalities (CA) after transcatheter aortic valve implantation (TAVI). AIMS: The aim of this study was to describe the morphology of the MS and predict the risk of new CA after TAVI based on the MS morphology and THV implantation depth. METHODS: Based on preprocedural CT scans, the MS depth was measured for every 25% of the entire MS width in 272 TAVI patients without preprocedural bundle branch block (BBB) or pacemaker. Post-procedural CT scans for THV implantation depth assessment were available in 130 of these patients. RESULTS: The MS depth was a median of 2.5 mm (IQR 1.4-3.8) deeper at the posterior edge when compared to the anterior edge of the MS. New CA developed in 7.1% of patients in whom the THV did not cross the lower MS border at its anterior edge (3.6% with new BBB and high degree CA, respectively), in 18.8% of patients (15.6% with new BBB and 3.1% with new high-degree CA) where the THV overlapped the lower MS border by <2.5 mm and in 47.1% of patients (24.3% with new BBB and 22.9% with new high-degree CA) with THV overlap of the lower MS border by ≥2.5 mm. CONCLUSIONS: The difference of the MS depth and THV implantation depth measured at the anterior edge of the MS predicted new CA after TAVI.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.004
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0010.000

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 head0.014
GPT teacher head0.298
Teacher spread0.284 · 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 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".

Quick stats

Citations32
Published2022
Admission routes1
Has abstractyes

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