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Record W2111578952 · doi:10.1093/eurheartj/ehu257

Open issues in transcatheter aortic valve implantation. Part 2: procedural issues and outcomes after transcatheter aortic valve implantation

2014· review· en· W2111578952 on OpenAlexaff
Jeroen J. Bax, Victoria Delgado, Vinayak Bapat, Helmut Baumgartner, Jean Collet, Raimund Erbel, Christian W. Hamm, A. Pieter Kappetein, Jonathon Leipsic, Martin B. Leon, Philip MacCarthy, Nicolò Piazza, Philippe Pîbarot, William C. Roberts, Josep Rodés‐Cabau, Patrick W. Serruys, Alec Vahanian, John G. Webb, José Luis Zamorano, Stephan Windecker

Bibliographic record

VenueEuropean Heart Journal · 2014
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsMcGill University Health CentreUniversité LavalSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineRegurgitation (circulation)ProsthesisCardiologyInternal medicineAortic valveSurgery

Abstract

fetched live from OpenAlex

This article provides an overview on procedure-related issues and uncertainties in outcomes after transcatheter aortic valve implantation (TAVI). The different access sites and how to select them in an individual patient are discussed. Also, the occurrence and potential predictors of aortic regurgitation (AR) after TAVI are addressed. The different methods to quantify AR are reviewed, and it appears that accurate and reproducible quantification is suboptimal. Complications such as prosthesis-patient mismatch and conduction abnormalities (and need for permanent pacemaker) are discussed, as well as cerebrovascular events, which emphasize the development of optimal anti-coagulative strategies. Finally, recent registries have shown the adoption of TAVI in the real world, but longer follow-up studies are needed to evaluate the outcome (but also prosthesis durability). Additionally, future studies are briefly discussed, which will address the use of TAVI in pure AR and lower-risk patients.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.682
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.006
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 head0.046
GPT teacher head0.405
Teacher spread0.359 · 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 designObservational
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

Citations115
Published2014
Admission routes1
Has abstractyes

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