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Record W4286763658 · doi:10.15420/icr.2021.19

Transcatheter Aortic Valve Implantation for Severe Pure Aortic Regurgitation with Dedicated Devices

2022· review· en· W4286763658 on OpenAlexaff
Pierluigi Costanzo, Paul Bamborough, Mark D. Peterson, Djeven P. Deva, Géraldine Ong, Neil Fam

Bibliographic record

VenueInterventional Cardiology Reviews Research Resources · 2022
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineRegurgitation (circulation)CardiologyInternal medicineAortic valvevalvular heart diseaseProsthesisSurgery

Abstract

fetched live from OpenAlex

Aortic regurgitation (AR) is not the most common valvular disease; however, its prevalence increases with age, with more than 2% of those aged >70 years having at least moderate AR. Once symptoms related to AR develop, the prognosis becomes poor. Transcatheter aortic valve implantation for patients with pure severe AR and at prohibitive surgical risk is occasionally performed, but remains a clinical challenge due to absence of valvular calcium, large aortic root and increased stroke volume. These issues make the positioning and deployment of transcatheter aortic valve implantation devices unpredictable, with a tendency to prosthesis embolisation or malposition. To date, the only two dedicated transcatheter valves for AR are the J-Valve (JC Medical) and the JenaValve (JenaValve Technology). Both devices have been used successfully via the transapical approach. The transfemoral experience is limited to first-in-human publications and to a clinical trial dedicated to AR, for which the completion date is still pending.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.171
GPT teacher head0.494
Teacher spread0.322 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations17
Published2022
Admission routes1
Has abstractyes

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