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Record W2205160139 · doi:10.2217/ica.15.8

Review of anticoagulation options for mechanical valve prosthesis

2015· article· en· W2205160139 on OpenAlexaff
Hadi Toeg, Munir Boodhwani

Bibliographic record

VenueInterventional Cardiology · 2015
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineMechanical valveMechanical heart-valveCardiologyProsthesisInternal medicineIntensive care medicineSurgeryHeart valve

Abstract

fetched live from OpenAlex

Clinical guidelines recommend lifelong oral anticoagulation (OAC) with warfarin in all patients with mechanical valves with variance in the target INR for patient associated risk factors, type of mechanical valve or implant position of the valve. Recent randomized controlled trials have demonstrated that clinicians may consider a lower OAC strategy (INR: 1.5–2.5) in low (thrombogenic) risk patients undergoing bileaflet mechanical valve replacement thereby achieving similar thromboprophylaxis yet minimizing bleeding events. Likewise, physicians may also consider a lowered OAC option in high (thrombogenic) risk patients undergoing bileaflet mechanical valve replacement yielding similar efficacy (avoidance of thromboembolic events) and improving safety (bleeding events). Finally, while advancement of novel oral anticoagulants (NOACs) has been swift in the realm of atrial fibrillation anticoagulation management, NOACs for mechanical valves are currently contraindicated due to evidence of increased thromboembolic and bleeding risk. Future studies comparing NOACs and warfarin along with newer mechanical valve construction are eagerly being awaited.

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)0.003
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 score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.004
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.219
GPT teacher head0.432
Teacher spread0.214 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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