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Balancing the Risks of Thrombosis and Bleeding Following Transcatheter Aortic Valve Implantation: Current State-of-Evidence

2016· review· en· W2292037632 on OpenAlexaff
Rishi Puri, Omar Abdul‐Jawad Altisent, Francisco Campelo‐Parada, María Del Trigo, Ander Regueiro, Josep Rodés‐Cabau

Bibliographic record

VenueCurrent Pharmaceutical Design · 2016
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedicineCardiologyInternal medicineStenosisWarfarinPercutaneous coronary interventionAortic valveAortic valve stenosisThrombosisAortic valve replacementSurgeryMyocardial infarctionAtrial fibrillation

Abstract

fetched live from OpenAlex

While transcatheter aortic valve implantation (TAVI) has rapidly evolved as an acceptable alternative to conventional surgical aortic valve replacement in elderly, high-risk surgical candidates with critical aortic stenosis, thrombotic and bleeding complications remain relatively frequent and potentially life-threatening. Thrombotic events during and following TAVI relate to the dynamic interplay between the systemic burden of atherosclerotic disease, atrial arrhythmias, device and native aortic valve interactions, as well as platelet and coagulation cascade activation. Bleeding in the acute setting relates primarily to access site vascular complications, but also appears related to pre-existing renal impairment and anemia. Current pre-, peri- and post-procedural anti-thrombotic regimens are empirical, based on expert consensus following extrapolation from the wealth of experience gleaned following percutaneous coronary intervention. However the complexities of the TAVI procedure, the high-risk clinical substrate and competing effects of anti-thrombotic regimens and bleeding risk are yet to be prospectively assessed in randomized clinical trials for defining evidence-based anti-thrombotic strategies.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.491
GPT teacher head0.560
Teacher spread0.069 · 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 designOther design
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

Citations5
Published2016
Admission routes1
Has abstractyes

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