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Record W2073358757 · doi:10.4176/090115

Antithrombotic Therapy in Patients with Prosthetic Heart Valves

2009· article· en· W2073358757 on OpenAlexaff
HA Mohamed

Bibliographic record

VenueLibyan Journal of Medicine · 2009
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Issues in Pregnancy
Canadian institutionsRegina General Hospital
Fundersnot available
KeywordsMedicineAntithromboticWarfarinMechanical heartAspirinThrombosisHeparinAnticoagulantHeart valveSurgeryLow molecular weight heparinCardiologyIntensive care medicineAtrial fibrillationInternal medicine

Abstract

fetched live from OpenAlex

Patients with mechanical valve prostheses require a lifelong anticoagulant treatment. The combined use of Warfarin and low-dose aspirin appears to reduce the risk of valve thrombosis and systemic embolism at a low risk of bleeding. The management of women with prosthetic heart valves during pregnancy poses a particular challenge, as there are no available controlled clinical trials to provide guidelines for effective antithrombotic therapy. Oral anticoagulants, such as Warfarin, cause foetal embryopathy; unfractionated heparin and low-molecular-weight heparin have been reported to be ineffective in preventing thromboembolic complications. This article discusses the available data and the most recent guidelines in the antithrombotic management of patients with prosthetic valves, and antithrombotic therapy in various clinical situations such as pregnant women with prosthetic heart valves, and patients with prosthetic heart valves undergoing noncardiac surgery.

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)0.000
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.263
Teacher spread0.254 · 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

Citations9
Published2009
Admission routes1
Has abstractyes

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