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Record W2577532082 · doi:10.1097/hco.0000000000000371

Sutureless aortic valves

2017· review· en· W2577532082 on OpenAlexaff
Amine Mazine, Christopher Bonneau, Dimos Karangelis, Bobby Yanagawa, Subodh Verma, D. Bonneau

Bibliographic record

VenueCurrent Opinion in Cardiology · 2017
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineAortic valve replacementStenosisSurgeryAortic valveInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Sutureless aortic valve replacement (AVR) has emerged as an alternative to traditional AVR for patients with aortic stenosis who present a higher surgical risk, such as the elderly, or those with small or highly calcified aortic roots. With transcatheter aortic valve implantation - the other major AVR alternative - being used in increasingly lower-risk patients, the place of sutureless valves in the AVR landscape needs to be defined. In this review, we discuss recent data and expert opinion as it pertains to the subject of sutureless AVR. RECENT FINDINGS: Several recent studies have evaluated the performance of sutureless valves in a variety of clinical contexts, including minimally invasive operations and high-risk surgical procedures. The optimal surgical technique for sutureless AVR has been refined through the publication of several reports addressing technical considerations. Reduction in operative times represents the main advantage of sutureless valves over conventional surgical prostheses, and the possibility of complete annular decalcification - and hence a reduced incidence of paravalvular leak - is the primary advantage over TAVI. SUMMARY: Sutureless valves have emerged as an attractive option for high-risk patients or for complex surgeries where a minimization of bypass time is critical. However, there is limited data regarding long-term outcomes, durability or reoperation.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.245
GPT teacher head0.546
Teacher spread0.302 · 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.

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

Citations15
Published2017
Admission routes1
Has abstractyes

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