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Record W2591709594 · doi:10.15420/icr.2016:25:2

Optimising the Haemodynamics of Aortic Valve-in-valve Procedures

2017· article· en· W2591709594 on OpenAlex
Ren Jie Robert Yao, Matheus Simonato, Danny Dvir

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInterventional Cardiology Reviews Research Resources · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsSt. Paul's Hospital
Fundersnot available
KeywordsMedicineHeart valveCardiologySurgeryStenosisAortic valveValve replacementAortic valve stenosisMechanical valveHemodynamicsInternal medicine

Abstract

fetched live from OpenAlex

Bioprosthetic surgical valves are increasingly implanted during cardiac surgery, instead of mechanical valves. These tissue valves are associated with limited durability and as a result transcatheter valve-in-valve procedures are performed to treat failed bioprostheses. A relatively common adverse event of aortic valve-in-valve procedures is residual stenosis. Larger surgical valve size, supra-annular transcatheter heart valve type, as well as higher transcatheter heart valve implantation depth, have all been shown to reduce the incidence of elevated post-procedural gradients. With greater understanding of technical considerations and surgical planning, valve-in-valve procedures could be more effective and eventually may become the standard of care for our increasingly ageing and comorbid population with failed surgical bioprostheses.

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.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.069
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.134
GPT teacher head0.503
Teacher spread0.369 · 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