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Record W2529959418 · doi:10.2147/ijgm.s109104

Minimizing bleeding risk in patients receiving direct oral anticoagulants for stroke prevention

2016· review· en· W2529959418 on OpenAlexaff
Jeffery S Habert

Bibliographic record

VenueInternational Journal of General Medicine · 2016
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineStroke (engine)RivaroxabanDabigatranWarfarinIntensive care medicineAtrial fibrillationInternal medicine

Abstract

fetched live from OpenAlex

Many primary care physicians are wary about using direct oral anticoagulants (DOACs) in patients with nonvalvular atrial fibrillation (AF). Factors such as comorbidities, concomitant medications, and alcohol misuse increase concerns over bleeding risk, especially in elderly and frail patients with AF. This article discusses strategies to minimize the risk of major bleeding events in patients with AF who may benefit from oral anticoagulant therapy for stroke prevention. The potential benefits of the DOACs compared with vitamin K antagonists, in terms of a lower risk of intracranial hemorrhage, are discussed, together with the identification of reversible risk factors for bleeding and correct dose selection of the DOACs based on a patient's characteristics and concomitant medications. Current bleeding management strategies, including the new reversal agents for the DOACs and the prevention of bleeding during preoperative anticoagulation treatment, in addition to health care resource use associated with anticoagulation treatment and bleeding, are also discussed. Implementing a structured approach at an individual patient level will minimize the overall risk of bleeding and should increase physician confidence in using the DOACs for stroke prevention in their patients with nonvalvular AF.

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)0.002
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 score0.967
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
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 head0.127
GPT teacher head0.438
Teacher spread0.311 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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