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Record W4298110292 · doi:10.5853/jos.2022.01697

Transcatheter Patent Foramen Ovale Closure in Stroke Patients with Thrombophilia: Current Status and Future Perspectives

2022· review· en· W4298110292 on OpenAlexaff
Julio I. Farjat‐Pasos, Jorge Nuche, Jules Mesnier, Vassili Panagides, Stéphanie Cloutier, Christine Houde, Josep Rodés‐Cabau

Bibliographic record

VenueJournal of Stroke · 2022
Typereview
Languageen
FieldMedicine
TopicCardiovascular and Diving-Related Complications
Canadian institutionsCentre hospitalier universitaire de QuébecUniversité Laval
Fundersnot available
KeywordsMedicinePatent foramen ovaleThrombophiliaPercutaneousStroke (engine)Closure (psychology)AntithromboticInternal medicineIntensive care medicineSurgeryPediatricsThrombosis

Abstract

fetched live from OpenAlex

Transcatheter patent foramen ovale (PFO) closure is a safe and effective treatment for secondary prevention after a PFO-associated stroke as demonstrated in multiple large randomized clinical trials. However, these trials excluded a significant proportion of patients who could have benefited from percutaneous PFO closure due to coexisting potential confounders such as additional thromboembolic risk factors, namely thrombophilia. Since scarce and conflicting data existed on such patients, current clinical management guidelines on patients with PFO mainly recommended against PFO closure in patients with thrombophilia and failed to provide any recommendation on the type and duration of antithrombotic treatment after transcatheter PFO closure. In the past 2 years, there has been new evidence supporting transcatheter PFO closure as a clinically meaningful alternative (vs. medical treatment) in this high-risk group of patients, along with additional data supporting the important role of systematic screening for thrombophilia in PFO-associated cerebrovascular events. This review article provides an updated overview of the incidence, clinical characteristics and outcomes of PFO closure in patients with thrombophilia, also commenting on the most appropriate medical treatment after PFO closure and future perspectives in the field.

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)0.000
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 score0.978
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.037
GPT teacher head0.292
Teacher spread0.255 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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