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Record W4389059929 · doi:10.21037/jovs-23-27

The future of sutureless valve technology

2023· article· en· W4389059929 on OpenAlexaff
Kirsten Allen, Adham El Sherbini, Mohammad El‐Diasty

Bibliographic record

VenueJournal of Visualized Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineVentricular outflow tractBicuspid valveCardiologyAortic valveAortic valve replacementInternal medicineBicuspid aortic valveSurgeryStenosis

Abstract

fetched live from OpenAlex

Abstract: The use of sutureless valves (SV) has been emerging as a promising surgical option in certain patient populations due to their straightforward and fast deployment technique and the encouraging mid- and long-term outcomes compared to surgical aortic valve replacement (SAVR) with stented valves. Main concerns associated with the use of this technology include increased rates of paravalvular leak (PVL) and a higher need for permanent pacemaker implantation (PPI); however, the reported outcomes have been improving over the years. This may be partly due to more surgeon exposure and experience in using these valves, a better understanding of the risk factors, and the improvement in the sizing techniques of these valves. The recently released new generation PercevalTM PLUS valve also offers an adapted design that reduces the protrusion of the valve into the left ventricular outflow tract (LVOT), which may further decrease the risk of damage to the conduction system. Recent studies have also shown supporting evidence that the use of SV may have potential in some complex situations, such as in patients with bicuspid aortic valves (BAV) and/or patients with dilated aortic annuli; however, these applications are yet to be endorsed by data derived from large-scale clinical studies. Future research focused on improving tissue durability and developing valve design may be key in the expansion of the use of this technology.

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)0.001
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 score0.079
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.396
Teacher spread0.380 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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