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Record W2069856510 · doi:10.1097/hco.0b013e32834dc34d

The use of anticoagulation during the periprocedure period of atrial fibrillation ablation

2011· review· en· W2069856510 on OpenAlexaff
Atul Verma, Bernice Tsang

Bibliographic record

VenueCurrent Opinion in Cardiology · 2011
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsSouthlake Regional Health Center
Fundersnot available
KeywordsMedicineAtrial fibrillationAblationCardiologyInternal medicineAblation of atrial fibrillationCatheter ablation

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Ablation is a treatment option for selected patients with atrial fibrillation that is being used more frequently, increasing the importance of awareness of both its risks and benefits. This review discusses the thromboembolic and bleeding risks during ablation, strategies to minimize these risks and use of long-term oral anticoagulation post ablation. RECENT FINDINGS: Thromboembolic and bleeding risks imparted by atrial fibrillation ablation can be minimized by echocardiography, optimal intraprocedural anticoagulation, and use of irrigated catheters and access sheaths with constant heparinized saline flow. Additionally, a strategic approach to periprocedural anticoagulation that may include continuation of warfarin, bridging with low-molecular-weight heparin (LMWH), or use of aspirin alone is essential in the balance of thrombotic and hemorrhagic risks. Novel anticoagulants (direct thrombin inhibitors or anti-Xa inhibitors) may add further options. SUMMARY: The use of atrial fibrillation ablation has increased over the past decade. Along with technique and technology advances that have improved the success of ablation, strides have been made in minimizing thromboembolic and bleeding risks and in the availability of a broader choice of anticoagulants. Research is ongoing to identify patients most suitable for ablation and to determine the long-term efficacy and safety of this treatment option.

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)0.001
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 score0.951
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.289
GPT teacher head0.424
Teacher spread0.135 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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