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Record W2626350536 · doi:10.4172/2368-0512.1000079

Management of anticoagulation in rate controlled atrial fibrillation: A three-step approach

2017· article· en· W2626350536 on OpenAlexvenueno aff
Christopher L. Hoehmann, Arron Gravina, Joshua A. Cuoco

Bibliographic record

VenueCurrent research. Cardiology · 2017
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAtrial fibrillationStroke (engine)Intensive care medicineManagement of atrial fibrillationClinical trialCardiologyClinical PracticeInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

Atrial Fibrillation is the most common type of cardiac arrhythmia managed in clinical practice. This condition predisposes patients to a multitude of potential complications that can be managed or prevented by medical or surgical means. Such complications include stroke, hemorrhage, or heart failure, among others. Although surgical intervention may be a viable option in some cases, pharmacological rate-control is a less invasive and more robust method in preventing the genesis of these sequelae. However, current guidelines regarding pharmacologic control of atrial fibrillation sequelae may be difficult to interpret. Based upon clinical prediction rules derived from large-scale clinical trials, we provide a simple three-step sequential algorithm that clinicians may use as guidelines when managing patients with rate controlled atrial fibrillation. These protocols can help clinicians decide if anticoagulation medication is indicated (CHA2DS2-VASc score), select the appropriate agent for anticoagulation (SAMe-TT2R2 score), and anticipate a high risk of hemorrhage (HAS-BLED score).

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.255
GPT teacher head0.462
Teacher spread0.208 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2017
Admission routes1
Has abstractyes

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