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Record W4416549652 · doi:10.1093/icvts/ivaf277

Simplified Surgical Conversion of Mechanical to Bioprosthetic Bentall With Leaflet Fracture Technique

2025· article· en· W4416549652 on OpenAlexaff
Yasuhiko Kawaguchi, J. D. S. Higgins, Kassem Ashe, Gary Salasidis

Bibliographic record

VenueInterdisciplinary CardioVascular and Thoracic Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsLondon Health Sciences CentreRegional Municipality of WaterlooWestern University
Fundersnot available
KeywordsPannusStenosisVentricular outflow tractAortic valveBentall procedureBicuspid valveProsthesis

Abstract

fetched live from OpenAlex

We report a case of simplified surgical conversion from a prior mechanical composite valved conduit to a bioprosthetic aortic valve using a leaflet fracture technique. A 69-year-old man presented with progressive heart failure 7 years after aortic root replacement for bicuspid aortic stenosis and root aneurysm. Imaging revealed severe prosthetic valve stenosis and suspected pannus formation. Given significant comorbidities, a simplified approach was chosen to avoid full root re-replacement. Following redo sternotomy and graft incision, the mechanical valve leaflets were fractured and removed. A Foley balloon inserted into the left ventricular outflow tract prevented leaflet embolization. Pannus excision revealed a hypertrophic subvalvular septum, prompting a septal myectomy. A 23-mm bioprosthetic valve was implanted above the retained mechanical housing using interrupted mattress sutures. The patient's postoperative course was uneventful, and echocardiography confirmed good valve function. This case highlights the utility of leaflet fracture as a safe and efficient option in high-risk reoperative settings and underscores the added benefit of direct subvalvular visualization for detecting underlying anatomic contributors to prosthetic dysfunction not detected preoperatively.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0000.000

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 head0.015
GPT teacher head0.298
Teacher spread0.283 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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