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Record W2964399996 · doi:10.3389/fcvm.2019.00120

Valve-in-Valve Challenges: How to Avoid Coronary Obstruction

2019· review· en· W2964399996 on OpenAlexaff
Fernando Luiz de Melo Bernardi, Danny Dvir, Josep Rodés‐Cabau, Henrique Barbosa Ribeiro

Bibliographic record

VenueFrontiers in Cardiovascular Medicine · 2019
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCardiologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Coronary obstruction is a rare but life-threatening complication in patients undergoing transcatheter aortic valve replacement (TAVR). Aortic valve-in-valve (VIV) procedures to treat failed surgical bioprosthesis is associated with ~6-fold higher risk for coronary obstruction in certain situations. The primary mechanism consists in the occlusion of the coronary ostium by the dislodged leaflet from the bioprosthesis after deployment of the transcatheter heart valve (THV), which most commonly occurs during the index procedure, but in up to 1/3 of cases a delayed presentation ensues. The clinical presentation consists of severe hypotension and ECG changes in most of the patients, with very high mortality rates. Therefore, pre-procedural multi-slice computed tomography is crucial for identifying high-risk features, such as low coronary heights, shallow sinuses of Valsalva, and short virtual THV to coronary ostial distance (VTC). Also, some models of surgical bioprosthesis present an increased risk for this dreadful complication. Preemptive protective strategies with coronary wiring, with or without placement of an undeployed stent, could mitigate the risks associated with this complication in high-risk patients, even though studies are lacking. This review aims to take a clinical perspective on the challenges in avoiding this complication during VIV procedures.

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)0.002
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.048
GPT teacher head0.340
Teacher spread0.291 · 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

Citations51
Published2019
Admission routes1
Has abstractyes

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