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Oral Anticoagulation Use in Individuals With Atrial Fibrillation and Chronic Kidney Disease: A Review

2024· review· en· W4396900023 on OpenAlexaff
Sara L. Wing, Thomas A. Mavrakanas, Ziv Harel

Bibliographic record

VenueSeminars in Nephrology · 2024
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of TorontoMcGill University Health CentreSt. Michael's Hospital
FundersServierGlaxoSmithKlineAstraZenecaPfizerBristol-Myers Squibb
KeywordsMedicineKidney diseaseAtrial fibrillationStroke (engine)Renal functionIntensive care medicineRandomized controlled trialPopulationDialysisInternal medicineApixabanWarfarinRivaroxaban

Abstract

fetched live from OpenAlex

Atrial fibrillation (AF) is highly prevalent in patients with chronic kidney disease (CKD). It is associated with an increased risk of stroke, which increases as kidney function declines. In the general population and in those with a moderate degree of CKD (creatinine clearance 30-50 mL/min), the use of oral anticoagulation to decrease the risk of stroke has been the standard of care based on a favorable risk-benefit profile that had been established in seminal randomized controlled trials. However, evidence regarding the use of oral anticoagulants for stroke prevention is less clear in patients with severe CKD (creatinine clearance <30 mL/min) and those receiving maintenance dialysis, as these individuals were excluded from such large randomized controlled trials. Nevertheless, the direct oral anticoagulants have invariably usurped vitamin K antagonists as the preferred choice for oral anticoagulation among patients with AF across all strata of CKD based on their well-defined safety and efficacy and multiple pharmacokinetic benefits (e.g., less drug-drug interactions). This review summarizes the current literature on the role of oral anticoagulation in the management of AF among patients with CKD and highlights current deficiencies in the evidence base and how to overcome them.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.890
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
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 head0.065
GPT teacher head0.376
Teacher spread0.312 · 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.

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
Published2024
Admission routes1
Has abstractyes

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