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Record W3047882241 · doi:10.14740/cr1108

The Issue of Subclinical Leaflet Thrombosis After Transcatheter Aortic Valve Implantation

2020· review· en· W3047882241 on OpenAlexvenueno aff
Dinaldo Cavalcanti de Oliveira, Sercan Okutucu, Giulio Russo, Estevao C. Campos Martins

Bibliographic record

VenueCardiology Research · 2020
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSubclinical infectionStenosisComplicationCardiologyInternal medicineThrombosisIncidence (geometry)SurgeryRadiology

Abstract

fetched live from OpenAlex

Transcatheter aortic valve implantation (TAVI) has been considered an important therapy for the treatment of symptomatic severe aortic stenosis. Although the devices and the techniques have been improved some complications may occur and several issues still need to be addressed. The issue of subclinical leaflet thrombosis (SLT) has been recognized as a complication after TAVI, and its incidence ranges from 0% to 40%. Nowadays, computed tomography is considered as the standard method for diagnosis of SLT. The concept of hypoattenuated leaflet thickening (HALT), reduced leaflet motion (RELM), and hypoattenuation affecting motion (HAM) have been used in this topic. Most patients who had SLT were taking single or dual antiplatelet therapy. In addition, these medications were not effective in resolving this complication after TAVI. However, there is a suggestion that oral anticoagulants have a protective and therapeutic effect. With the increasing use of TAVI, it is necessary to have better knowledge about several aspects of this complication, because it may have impact on prognosis. Therefore, some aspects of SLT diagnosis, management, and prognosis are not yet fully understood.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.009
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.165
GPT teacher head0.539
Teacher spread0.373 · 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 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

Citations9
Published2020
Admission routes1
Has abstractyes

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